The Machine Learning for Social Science Global Subnational Covid Counts Datset
Rex W. Douglass
Introduction
Library Loads
#libraries
library(lubridate)
library(tidyverse)
#devtools::install_github("ropensci/USAboundaries")
#devtools::install_github("ropensci/USAboundariesData")
library(USAboundaries) ; #install.packages('USAboundaries')
data(state_codes)
library(tidyverse)
library(scales)
library(gghighlight)
library(lubridate)
library(R0) # consider moving all library commands to top -- this one was in a loop below
library(WikidataR)
library(countrycode)
library(usmap) ; # install.packages('usmap')
data(statepop)
#devtools::install_github("ropensci/USAboundaries")
#devtools::install_github("ropensci/USAboundariesData")
library(USAboundaries) ; #install.packages('USAboundaries')
data(state_codes)
library(tidyverse)
library(sf)
library(jsonlite)
#This is too slow it's downloading each
library(GADMTools)
library(strucchange) ; #install.packages('strucchange')
library(tsibble)Data Loading and Cleaning
library(patchwork)
library(tsibble)
lhs_long <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long.Rds")
#dim(lhs_long) #187,305
lhs_long_clean <- lhs_long %>%
dplyr::select(dataset, gid,geonameid, wikidata_id ,date_asdate, confirmed, deaths, tested_people, tested_samples) %>%
group_by(dataset, gid,geonameid, wikidata_id ,date_asdate) %>% #if the same source has multiple values on the same thing on the same day, or different values across unmerged obs, then take the max
summarise_all(max, na.rm=T) %>%
ungroup() %>%
mutate_if(is.numeric, list(~na_if(., -Inf))) %>%
mutate_if(is.numeric, list(~na_if(., Inf))) %>%
mutate(dataset_gid_geonameid_wikidata_id= paste(dataset,gid,geonameid, wikidata_id, sep="_") ) %>%
mutate(gid_geonameid_wikidata_id= paste(gid,geonameid, wikidata_id, sep="_") ) %>%
filter(!is.na(date_asdate)) %>%
#First drop anything that's negative. Shouldn't be any negatives.
filter(is.na(confirmed) | confirmed>=0) %>%
filter(is.na(deaths) | deaths>=0) %>%
filter(is.na(tested_people) | tested_people>=0) %>%
filter(is.na(tested_samples) | tested_samples>=0) %>%
#Then set 0s to NA, we don't trust NAs
mutate(confirmed=ifelse(confirmed==0, NA, confirmed)) %>%
mutate(deaths=ifelse(deaths==0, NA, deaths)) %>%
mutate(tested_people=ifelse(tested_people==0, NA, tested_people)) %>%
mutate(tested_samples=ifelse(tested_samples==0, NA, tested_samples)) %>%
#Then check first differences and drop anything that doesn't weakly increase monotonically
group_by(dataset, gid_geonameid_wikidata_id) %>%
arrange(date_asdate) %>%
mutate(confirmed_cummax=confirmed %>% replace_na(0) %>%
cummax(), deaths_cummax=deaths%>% replace_na(0) %>% cummax(), tested_people_cummax=tested_people%>% replace_na(0) %>% cummax(),
tested_samples_cummax=tested_samples%>% replace_na(0) %>% cummax()) %>%
ungroup() %>%
#About 4k of these observations show a decrease from one time step to the next which suggests either an original error or a joining error
#We're going to straight drop those observations as a cleaning step
#this isn't enough because if you have consecutive mistakes it'll show a positive fd even if it's not good enough
filter( (is.na(confirmed) | confirmed>=confirmed_cummax) & #never allow for a reversal
(is.na(deaths) | deaths>=deaths_cummax) & #never allow for a reversal
(is.na(tested_people) | tested_people>=tested_people_cummax) & #never allow for a reversal
(is.na(tested_samples) | tested_samples>=tested_samples_cummax) #never allow for a reversal
) %>%
#we're going to go one step further and require either confirmed or tested to be strictly higher
#In words, during an episode we don't believe reports mean "no new cases" just no new good reporting
#metabiota is the worst offender here so restricting it to just that
filter( !(
dataset=="metabiota" & #is metabiota
(!is.na(confirmed) & #with a non missing confirmed
confirmed<=confirmed_cummax ) #that is equal to or less than the cumulative max until then.
) #reject these
) %>% #metabiota is really problematic 90% of what we're doing is trying to account for it
#Also reject any where deaths are greater than confirmed
filter(is.na(confirmed) | is.na(deaths) | confirmed>=deaths) %>%
#also reject any where confirmed doesn't vary
group_by(gid , geonameid ,wikidata_id) %>% #chose not to do by dataset because testing datasets might not have confirmed
filter(var(confirmed, na.rm=T)>0) %>%
ungroup()
#dim(lhs_long_clean) #281,464 #292,580 #299213 #181,563
saveRDS(lhs_long_clean,
"/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_cleand.Rds")
Summary Statistics of Our Data
print("Number of observations")[1] "Number of observations"dim(lhs_long_clean)[1] 615638 15print("Number of locations")[1] "Number of locations"lhs_long_clean$wikidata_id %>% unique() %>% length() #3,373[1] 4263print("Number of Days")[1] "Number of Days"lhs_long_clean$date_asdate %>% unique() %>% length() #113[1] 128library(DT) #https://rstudio.github.io/DT/
lhs_long_clean %>% count(dataset) %>% arrange(-n) %>% DT::datatable(options = list(pageLength = 20, autoWidth = TRUE))
#library(gt)
#lhs_long_clean %>% count(dataset) %>% arrange(-n) %>% gt() %>%
# tab_header(
# title = md("Location-Days by Dataset")#,
# #subtitle = "Number of Days with Data by Dataset"
# ) %>%
# fmt_missing( columns=everything(), rows=NULL,missing_text = 0)
Spatial Variation in Data Availability
Country Level Data Availability
#gadm36 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESSgadm/data_in/gadm36_gpkg/gadm36.gpkg")
#st_layers("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg")
gadm36_levels_0 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg", layer="level0") %>%
st_simplify(preserveTopology = FALSE, dTolerance =0.1) # 0.025 this is supposedly broken up by 6 levels and so should have u.s.
#plot(gadm36_levels_0)
#dim(gadm36_levels_0_sf$sf)
#gadm_plot(gadm36_levels_0_sf)
lhs_long_place_sources <- lhs_long %>% dplyr::select(gid, geonameid, wikidata_id, dataset) %>%
group_by(gid, geonameid, wikidata_id) %>% count(dataset) %>%
group_by(gid, geonameid, wikidata_id) %>%
summarise(datasets_n=n())
p0 <- gadm36_levels_0 %>%
left_join(lhs_long_place_sources %>% dplyr::select(gid=gid, datasets_n) %>% left_join( gadm36_levels_0 %>% as.data.frame() %>% dplyr::select(gid=GID_0, NAME_0) %>% distinct() )
) %>%
#replace_na(list(datasets_n = 0)) %>%
ggplot() + geom_sf(aes(fill = datasets_n)) +
scale_fill_gradient(low="red", high="green") +
theme_bw() p0 + ggtitle("Covid Count Data Availability at the Country Level")
temp <- gadm36_levels_0 %>%
left_join(
lhs_long %>% count(gid, dataset) %>% rename(GID_0=gid) #there are still dupe geonames wikidata to gid matches that need to be fixed geonameid, wikidata_id,
) Joining, by = "GID_0"
Column `GID_0` joining factor and character vector, coercing into character vectortemp_wide <- temp %>% as.data.frame() %>% dplyr::select(Country=NAME_0, dataset, n) %>% distinct() %>% pivot_wider( id_cols = Country, names_from = dataset,
values_from = n, values_fill = NULL, values_fn = NULL)
temp_wide %>% DT::datatable(options = list(pageLength = 20, autoWidth = TRUE))
#install.packages('gt')
#library(gt)
#temp_wide %>% gt() %>%
# tab_header(
# title = md("Data Availability by Country"),
# subtitle = "Number of Days with Data by Dataset"
# ) %>%
# fmt_missing( columns=everything(), rows=NULL,missing_text = 0)
State/Province Level Data Availability
gadm36_levels_1 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg", layer="level1") %>%
st_simplify(preserveTopology = FALSE, dTolerance =0.1) # 0.025 this is supposedly broken up by 6 levels and so should have u.s.
p1 <- gadm36_levels_1 %>%
left_join(lhs_long_place_sources %>% dplyr::select(gid=gid, datasets_n) %>% left_join( gadm36_levels_1 %>% as.data.frame() %>% dplyr::select(gid=GID_1, NAME_1) %>% distinct() )
) %>%
#replace_na(list(datasets_n = 0)) %>%
ggplot() + geom_sf(aes(fill = datasets_n)) +
scale_fill_gradient(low="red", high="green") +
theme_bw() p1
temp <- gadm36_levels_1 %>%
left_join(
lhs_long %>% count(gid, dataset) %>% rename(GID_1=gid) #there are still dupe geonames wikidata to gid matches that need to be fixed geonameid, wikidata_id,
) Joining, by = "GID_1"
Column `GID_1` joining factor and character vector, coercing into character vectortemp_wide <- temp %>% as.data.frame() %>% dplyr::select(Country=NAME_0, Admin1=NAME_1, dataset, n) %>% distinct() %>% pivot_wider( id_cols = Country:Admin1, names_from = dataset,
values_from = n, values_fill = NULL, values_fn = NULL) %>% mutate(total = rowSums(.[-c(1,2)], na.rm = T))
temp_wide %>% filter(total>0) %>% DT::datatable(options = list(pageLength = 10, autoWidth = TRUE))
NACounty District Level Data Availability
gadm36_levels_2 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg", layer="level2") %>%
st_simplify(preserveTopology = FALSE, dTolerance = 0.001) # 0.025 this is supposedly broken up by 6 levels and so should have u.s. I have to keep shrinking it so misisng goes to zero
p2 <- gadm36_levels_2 %>%
left_join(lhs_long_place_sources %>% dplyr::select(gid=gid, datasets_n) %>% left_join( gadm36_levels_2 %>% as.data.frame() %>% dplyr::select(gid=GID_2, NAME_2) %>% distinct() )
) %>%
#replace_na(list(datasets_n = 0)) %>%
ggplot() + geom_sf(aes(fill = datasets_n),lwd = 0) +
scale_fill_gradient(low="blue", high="red") +
theme_bw() p2temp <- gadm36_levels_2 %>%
left_join(
lhs_long %>% count(gid, dataset) %>% rename(GID_2=gid) #there are still dupe geonames wikidata to gid matches that need to be fixed geonameid, wikidata_id,
) Joining, by = "GID_2"
Column `GID_2` joining factor and character vector, coercing into character vectortemp_wide <- temp %>% as.data.frame() %>% dplyr::select(Country=NAME_0, Admin1=NAME_1, Admin2=NAME_2, dataset, n) %>% distinct() %>% pivot_wider( id_cols = Country:Admin2, names_from = dataset,
values_from = n, values_fill = NULL, values_fn = NULL) %>% mutate(total = rowSums(.[-c(1,2,3)], na.rm = T))
temp_wide %>% filter(total>0) %>% DT::datatable(options = list(pageLength = 10, autoWidth = TRUE))It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlIt seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlCountry Data Availability of Testing
lhs_long_place_sources_testing <- lhs_long %>%
dplyr::select(gid, geonameid, wikidata_id, date_asdate, confirmed, tested_people, tested_samples) %>%
group_by(gid, geonameid, wikidata_id, date_asdate) %>%
mutate_if(is.numeric, max, na.rm=T) %>%
filter(!duplicated(date_asdate)) %>%
ungroup() %>%
mutate_if(is.numeric, list(~na_if(., -Inf))) %>%
mutate_if(is.numeric, list(~na_if(., Inf))) %>%
mutate(percent_of_days_without_testing=!is.na(date_asdate) & (is.na(tested_people) & is.na(tested_samples) )) %>%
group_by(gid, geonameid, wikidata_id) %>%
summarise(
percent_of_days_without_testing=sum(percent_of_days_without_testing, na.rm=T)/n()
) %>%
mutate(percent_of_days_with_testing= 1-percent_of_days_without_testing) `mutate_if()` ignored the following grouping variables:
Columns `gid`, `geonameid`, `wikidata_id`, `date_asdate`p0_testing <- gadm36_levels_0 %>%
left_join(lhs_long_place_sources_testing %>% dplyr::select(gid=gid, percent_of_days_with_testing) %>% left_join( gadm36_levels_0 %>% as.data.frame() %>% dplyr::select(gid=GID_0, NAME_0) %>% distinct() )
) %>%
#replace_na(list(datasets_n = 0)) %>%
ggplot() + geom_sf(aes(fill = percent_of_days_with_testing)) +
scale_fill_gradient(low="red", high="green") +
theme_bw() + ggtitle("Percent of Days with Testing counts")Adding missing grouping variables: `geonameid`
Joining, by = "gid"
Column `gid` joining character vector and factor, coercing into character vectorJoining, by = "NAME_0"p0_testing
p1_testing <- gadm36_levels_1 %>%
left_join(lhs_long_place_sources_testing %>% dplyr::select(gid=gid, percent_of_days_with_testing) %>% left_join( gadm36_levels_1 %>% as.data.frame() %>% dplyr::select(gid=GID_1, NAME_1) %>% distinct() )
) %>%
#replace_na(list(datasets_n = 0)) %>%
ggplot() + geom_sf(aes(fill = percent_of_days_with_testing)) +
scale_fill_gradient(low="red", high="green") +
theme_bw() + ggtitle("Percent of Days with Testing counts")p1_testing
Data Availability and Interpolation over Time
#test <- lhs_long %>% group_by(dataset, gid, geonameid, wikidata_id, date_asdate) %>% summarize(n=n())
all_na <- function(x) any(!is.na(x)) #https://intellipaat.com/community/12999/remove-columns-from-dataframe-where-all-values-are-na
#bing looks off by a day from the other ones
lhs_wide_qcode <- lhs_long %>%
filter(!is.na(wikidata_id) & !is.na(date_asdate)) %>%
group_by(gid, geonameid, wikidata_id, date_asdate) %>% mutate(confirmed_var=var(confirmed, na.rm=T), deaths_var=var(deaths, na.rm=T)) %>% ungroup() %>%
dplyr::select(dataset, gid, geonameid, wikidata_id, date_asdate,confirmed, deaths, tested_samples, tested_people) %>%
group_by(dataset, gid, geonameid, wikidata_id, date_asdate) %>% summarise_if(is.numeric,max,na.rm=T) %>% ungroup() %>% #this is a hack, we should have dupes within datasets
pivot_wider(names_from = dataset, values_from = c(confirmed, deaths, tested_samples, tested_people) ) %>%
mutate_if(is.numeric, list(~na_if(abs(.), Inf))) %>% #https://stackoverflow.com/questions/12188509/cleaning-inf-values-from-an-r-dataframe
select_if(all_na)
dim(lhs_wide_qcode) #82,157 82k place/day observations
length(unique(lhs_wide_qcode$wikidata_id)) #3697 #almost 4k
test <- lhs_wide_qcode %>% dplyr::select(starts_with("confirmed")) %>% distinct()
#cor(test, use="pairwise.complete.obs")
lhs_long_median <- lhs_long %>% group_by(gid, geonameid, wikidata_id, date_asdate) %>% summarize_if(is.numeric, median, na.rm=T) %>% dplyr::select(-ends_with("_fd")) %>% #we take the median across observations
mutate(CFR=deaths/confirmed)
#dim(lhs_long_median)
#summary(lhs_long_median)Here we do the actual interpolation. For each individual location-dataset, we fit a piecewise linear model over time, and then use that to average over day to day noise and interpolate between observations. Because this is in log space, the slope of that line is the day on day percent increase in the count. We finish by fitting a piecewise intercept model to those slopes to get a single daily estimate of how much the count is increasing. Datasets are able to disagree in lots of different ways and we still recover a correct rate of change.
Interpolation of Counts
To each individual dataset-location series, we fit a piecewise linear trend. Each segment is required to have at least 3 observations, but otherwise we make no other constraints. This allows us to flexibly fit linear segments as well as tight curves. We also allow for structural breaks where the series stops, has a structural shift up or down because say a change in reporting rules, but other then otherwise continues as normal. We take the linear trend fit as our best estimate at each point. This averages over day to day noise, e.g. under reporting on the weekend. It also allows us to interpolate between observations where there is missingness in the series but we have reasonable beliefs the true measure continued linearly between the observed points.
#we need to parallelize this code and see what's loudly throwing
#Error in chol.default(J12) :
# the leading minor of order 1 is not positive definite
dataset_places <- lhs_long_clean %>% dplyr::select(dataset_gid_geonameid_wikidata_id) %>% distinct() %>% pull(dataset_gid_geonameid_wikidata_id)
library(party)
temp_list1 <- list()
options(error = expression(NULL))
sink(NULL)
zz <- file("messages.txt", open = "wt")
sink(zz)
suppressMessages(
for(q in dataset_places ){
#try({
temp <- NULL
temp <- lhs_long_clean %>%
filter(dataset_gid_geonameid_wikidata_id %in% q) %>%
arrange(date_asdate) %>%
mutate(confirmed_log=log(confirmed)) %>%
mutate(deaths_log=log(deaths)) %>%
mutate(tested_people_log=log(tested_people)) %>%
mutate(tested_samples_log=log(tested_samples))
if(nrow(temp)==0){next()}
temp <- temp %>% expand(dataset, gid_geonameid_wikidata_id, gid, geonameid,wikidata_id, date_asdate=min(date_asdate):max(date_asdate) %>% as_date() ) %>%
full_join(temp) %>%
mutate(date_asnumeric= as.numeric(date_asdate) ) %>%
arrange(date_asnumeric) %>%
mutate(confirmed_nonmissing_count = (!is.na(confirmed)) %>% cumsum() ) %>%
mutate(deaths_nonmissing_count = (!is.na(deaths)) %>% cumsum() ) %>%
mutate(tested_people_nonmissing_count = (!is.na(tested_people)) %>% cumsum() ) %>%
mutate(tested_samples_nonmissing_count = (!is.na(tested_samples)) %>% cumsum() ) %>%
arrange(-date_asnumeric) %>%
mutate(confirmed_nonmissing_count_reversed = (!is.na(confirmed)) %>% cumsum() ) %>%
mutate(deaths_nonmissing_count_reversed = (!is.na(deaths)) %>% cumsum() ) %>%
mutate(tested_people_nonmissing_count_reversed = (!is.na(tested_people)) %>% cumsum() ) %>%
mutate(tested_samples_nonmissing_count_reversed = (!is.na(tested_samples)) %>% cumsum() ) %>%
arrange(date_asdate)
tryCatch({
if(sum(!is.na(temp$confirmed_log))>=3){ #need at least 3 points
tree_confirmed <- party::mob(confirmed_log ~ 1 + date_asnumeric | date_asnumeric , control = mob_control(minsplit = 3, alpha=.2), data = temp, model = linearModel) # + I(date_asnumeric^2)
temp$confirmed_log_y_hat <- predict( tree_confirmed, newdata=temp , type = c("response") )
temp$confirmed_log_group_number <- predict( tree_confirmed, newdata=temp , type = c("node"))
temp <- temp %>%
mutate(confirmed_log_y_hat =ifelse(confirmed_nonmissing_count>0 & confirmed_nonmissing_count_reversed>0,confirmed_log_y_hat,NA)) %>% #only interpolate within the observed data, never before or after
mutate(confirmed_log_group_number=ifelse(confirmed_nonmissing_count>0 & confirmed_nonmissing_count_reversed>0,confirmed_log_group_number,NA)) %>% #only interpolate within the observed data, never before or after
group_by(confirmed_log_group_number) %>%
arrange(date_asnumeric) %>%
mutate(confirmed_log_y_hat_slope=tsibble::difference(confirmed_log_y_hat)) %>%
fill(confirmed_log_y_hat_slope, .direction="up") %>%
ungroup() %>%
mutate(confirmed_log_y_hat_percent_change = round((exp(confirmed_log_y_hat_slope)-1)*100,2)) %>%
mutate(confirmed_log_y_hat=ifelse(confirmed_log_y_hat<0,NA, confirmed_log_y_hat))
}
}, error = function(e) {}, silent=TRUE)
tryCatch({
if(sum(!is.na(temp$deaths_log))>=3){ #need at least 3 points
tree_deaths <- party::mob(deaths_log ~ 1 + date_asnumeric | date_asnumeric , control = mob_control(minsplit = 3, alpha=.2), data = temp, model = linearModel) # + I(date_asnumeric^2)
temp$deaths_log_y_hat <- predict( tree_deaths, newdata=temp , type = c("response") )
temp$deaths_log_group_number <- predict( tree_deaths, newdata=temp , type = c("node"))
temp <- temp %>%
mutate(deaths_log_y_hat=ifelse(deaths_nonmissing_count>0 & deaths_nonmissing_count_reversed>0,deaths_log_y_hat,NA)) %>% #only interpolate within the observed data, never before
mutate(deaths_log_group_number=ifelse(deaths_nonmissing_count>0 & deaths_nonmissing_count_reversed>0,deaths_log_group_number,NA)) %>% #only interpolate within the observed data, never before
group_by(deaths_log_group_number) %>%
arrange(date_asnumeric) %>%
mutate(deaths_log_y_hat_slope=tsibble::difference(deaths_log_y_hat)) %>%
fill(deaths_log_y_hat_slope, .direction="up") %>%
ungroup() %>%
mutate(deaths_log_y_hat_percent_change = round((exp(deaths_log_y_hat_slope)-1)*100,2)) %>%
mutate(deaths_log_y_hat=ifelse(deaths_log_y_hat<0,NA, deaths_log_y_hat)) #reject any where the prediction is less than 1 full case
}
}, error = function(e) {}, silent=TRUE)
tryCatch({
if(sum(!is.na(temp$tested_people_log))>=3){ #need at least 3 points
tree_tested_people <- party::mob(tested_people_log ~ 1 + date_asnumeric | date_asnumeric , control = mob_control(minsplit = 3, alpha=.2), data = temp, model = linearModel) # + I(date_asnumeric^2)
temp$tested_people_log_y_hat <- predict( tree_tested_people, newdata=temp , type = c("response") )
temp$tested_people_log_group_number <- predict( tree_tested_people, newdata=temp , type = c("node"))
temp <- temp %>%
mutate(tested_people_log_y_hat=ifelse(tested_people_nonmissing_count>0 & tested_people_nonmissing_count_reversed>0,tested_people_log_y_hat,NA)) %>% #only interpolate within the observed data, never before
mutate(tested_people_log_group_number=ifelse(tested_people_nonmissing_count>0 & tested_people_nonmissing_count_reversed>0,tested_people_log_group_number,NA)) %>% #only interpolate within the observed data, never before
group_by(tested_people_log_group_number) %>%
arrange(date_asnumeric) %>%
mutate(tested_people_log_y_hat_slope=tsibble::difference(tested_people_log_y_hat)) %>%
fill(tested_people_log_y_hat_slope, .direction="up") %>% ungroup() %>%
mutate(tested_people_log_y_hat_percent_change = round((exp(tested_people_log_y_hat_slope)-1)*100,2)) %>%
mutate(tested_people_log_y_hat=ifelse(tested_people_log_y_hat<0,NA, tested_people_log_y_hat)) #reject any where the prediction is less than 1 full case
}
}, error = function(e) {}, silent=TRUE)
temp_list1[[q ]] <- temp
#})
}
)
sink(NULL)
lhs_long_clean_imputed1 <- bind_rows(temp_list1)
dim(lhs_long_clean_imputed1) #500,786 #bigger because we're interpolating now
#rt1 <- rpart(formula = confirmed_log_y_hat_percent_change ~ date_asnumeric, data=test)
#interpolated = data.frame(date_asnumeric=min(test$date_asnumeric):max(test$date_asnumeric) )
#interpolated$confirmed_log_y_hat_percent_change_y_hat <- predict(rt1, newdata=interpolated)
#interpolated$date_asdate <- as.Date(interpolated$date_asnumeric)
places <- lhs_long_clean %>% dplyr::select(gid_geonameid_wikidata_id) %>% distinct() %>% pull(gid_geonameid_wikidata_id)
library(rpart)
minsplit=6
minbucket=3
temp_list2 <- list()
for(q in places ){
print(q)
temp <- lhs_long_clean_imputed1 %>%
filter(gid_geonameid_wikidata_id %in% q) %>%
arrange(date_asnumeric)
tryCatch({
rt1 <- rpart::rpart(formula = confirmed_log_y_hat_percent_change ~ date_asnumeric,
data=temp %>% filter(!is.na(confirmed_log)), #to keep from fitting to interpolate slopes that are out of domain and usually wrong, require confirmed to not be missing or na
control = rpart.control(minsplit = minsplit, minbucket=minbucket))
temp$confirmed_log_y_hat_percent_change_y_hat <- predict(rt1, newdata=temp) #this prediction helpfully tries to impute backwards even before there are any day in any series
#We need to nuke it if no data exist
temp <- temp %>%
group_by(date_asdate) %>%
mutate( confirmed_nonmissing_count_datasets=sum(confirmed_nonmissing_count>0,na.rm=T) ) %>%
mutate( confirmed_nonmissing_count_datasets_reversed=sum(confirmed_nonmissing_count_reversed>0,na.rm=T) ) %>%
mutate(confirmed_log_y_hat_percent_change_y_hat=ifelse(confirmed_nonmissing_count_datasets>0 & confirmed_nonmissing_count_datasets_reversed>0,confirmed_log_y_hat_percent_change_y_hat,NA)) %>%
ungroup()
}, error = function(e) {})
tryCatch({
rt2 <- rpart::rpart(formula = deaths_log_y_hat_percent_change ~ date_asnumeric,
data=temp %>% filter(!is.na(deaths_log)),
control = rpart.control(minsplit = minsplit, minbucket=minbucket))
temp$deaths_log_y_hat_percent_change_y_hat <- predict(rt2, newdata=temp)
temp <- temp %>%
group_by(date_asdate) %>%
mutate( deaths_nonmissing_count_datasets=sum(deaths_nonmissing_count>0,na.rm=T) ) %>%
mutate( deaths_nonmissing_count_datasets_reversed=sum(deaths_nonmissing_count_reversed>0,na.rm=T) ) %>%
mutate(deaths_log_y_hat_percent_change_y_hat=ifelse(deaths_nonmissing_count_datasets>0 & deaths_nonmissing_count_datasets_reversed>0,deaths_log_y_hat_percent_change_y_hat,NA)) %>%
ungroup()
}, error = function(e) {})
tryCatch({
rt3 <- rpart::rpart(formula = tested_people_log_y_hat_percent_change ~ date_asnumeric,
data=temp %>% filter(!is.na(tested_people_log)),
control = rpart.control(minsplit = minsplit, minbucket=minbucket))
temp$tested_people_log_y_hat_percent_change_y_hat <- predict(rt3, newdata=temp, control = rpart.control(minsplit = minsplit, minbucket=minbucket))
temp <- temp %>%
group_by(date_asdate) %>%
mutate( tested_people_nonmissing_count_datasets=sum(tested_people_nonmissing_count>0,na.rm=T) ) %>%
mutate( tested_people_nonmissing_count_datasets_reversed=sum(tested_people_nonmissing_count_reversed>0,na.rm=T) ) %>%
mutate(tested_people_log_y_hat_percent_change_y_hat=ifelse(tested_people_nonmissing_count_datasets>0 & tested_people_nonmissing_count_datasets_reversed>0,tested_people_log_y_hat_percent_change_y_hat,NA)) %>%
ungroup()
}, error = function(e) {})
temp_list2[[q ]] <- temp
}
lhs_long_clean_imputed2 <- bind_rows(temp_list2)
lhs_long_clean_imputed <- lhs_long_clean_imputed2
dim(lhs_long_clean_imputed) #629,493 #419910 #413475 #408,369 #we lose anywithout q codes here
saveRDS(lhs_long_clean_imputed, "/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_clean_imputed.Rds")
Examples
China
#China
#"Q148"
test <- lhs_long_clean_imputed %>%
mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q148") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation0 <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("China Q148 Confirmed") + facet_wrap(~dataset )
Data on China shows variation across datasets for when they start measuring. Only two observe the very beginning of the outbreak. All of the datasets show a discontinuity where reporting standards must have changed. Our method attempts to bridge that gap in 3 of the series but not two others.
p_interpolation0
Italy
#Italy
#"Q38"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation1 <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Italy Q38 Confirmed") + facet_wrap(~dataset )
Italy likewise shows variation in the start of recording across datasets. There’s also variation in the initial sparcity. Wikipedia is strangely missing data in the middle. Our method linearly interpolates into that space in a way in a way we probably don’t want.
p_interpolation1
Italy
#US
#"Q30"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q30") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation2 <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("US Q30 Confirmed") + facet_wrap(~dataset )
p_interpolation2
New York State
#US
#"Q30"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q1384") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation3 <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("New York State Q1384 Confirmed") + facet_wrap(~dataset )
p_interpolation3
New York City (by county)
nyt has it just by city, it doesn’t break it down by the boroughs. But because gadm doesn’t do cities we don’t assign it a qcode.
usafacts has it as New York County which is New York County (Manhattan)
#Brooklyn (Q18419)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q855974") %>% #no hits?
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation4a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Bronx County (Q855974)") + facet_wrap(~dataset ) + guides( color = FALSE)
#Queens County, New York Q5142559
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q5142559") %>% #no hits?
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation4b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Queens County, New York Q5142559") + facet_wrap(~dataset ) + guides( color = FALSE)
#Brooklyn is aparently Kings County Q11980692
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q11980692") %>% #no hits?
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation4c <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Brooklyn is Kings County Q11980692") + facet_wrap(~dataset ) + guides( color = FALSE)
# Manhattan is New York County Q500416
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q500416") %>% #no hits?
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation4d <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Manhattan is New York County Q500416") + facet_wrap(~dataset ) + guides( color = FALSE)
#Staten Island is richmond county Q11997784
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q11997784") %>% #no hits?
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p_interpolation4e <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Staten Island Richmond County Q11997784") + facet_wrap(~dataset ) + guides( color = FALSE)
Note that CSSE is conflating all of New York City with Manhattan/New York County, Bing is too I think
p_interpolation4a + p_interpolation4b + p_interpolation4c + p_interpolation4d + p_interpolation4e
Plots by Countries
library(data.table)
geonames <- fread("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/allCountries.txt", sep="\t") %>% mutate_if(is.numeric,as.character) %>% mutate_if(is.factor,as.character)|--------------------------------------------------|
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Found and resolved improper quoting out-of-sample. First healed line 522433: <<8436127 "Baki-Pilsen" Brewery "Baki-Pilsen" Brewery 40.32864 49.78044 P PPLL AZ 0 -25 Asia/Baku 2012-12-17>>. If the fields are not quoted (e.g. field separator does not appear within any field), try quote="" to avoid this warning.dim(geonames) #12,006,426[1] 12006426 19geonames_small <- geonames %>% dplyr::select(geonameid=V1, name_text=V2) %>% inner_join( lhs_long_clean_imputed %>% dplyr::select(geonameid) %>% distinct() )Joining, by = "geonameid"dim(geonames_small)[1] 4108 2Confirmed
p_confirmed_by_country <- lhs_long_clean_imputed %>%
filter(!gid %>% str_detect("_") ) %>%
left_join(geonames_small ) %>% mutate(name_text %>% as.factor()) %>%
mutate(group=paste(gid_geonameid_wikidata_id,confirmed_log_group_number)) %>%
ggplot() +
#geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Confirmed (log) by Country") + facet_wrap(~name_text ) + guides( color = FALSE) +
theme(
strip.background = element_blank()
, strip.text.x = element_blank()
)p_confirmed_by_country
Deaths
p_deaths_by_country <- lhs_long_clean_imputed %>%
filter(!gid %>% str_detect("_") ) %>%
left_join(geonames_small ) %>% mutate(name_text %>% as.factor()) %>%
mutate(group=paste(gid_geonameid_wikidata_id,confirmed_log_group_number)) %>%
ggplot() +
#geom_point( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Deaths (log) by Country") + facet_wrap(~name_text ) + guides( color = FALSE) +
theme(
strip.background = element_blank()
, strip.text.x = element_blank()
)p_deaths_by_country
Tests
p_tested_people_by_country <- lhs_long_clean_imputed %>%
filter(!gid %>% str_detect("_") ) %>%
left_join(geonames_small ) %>% mutate(name_text %>% as.factor()) %>%
mutate(group=paste(gid_geonameid_wikidata_id,confirmed_log_group_number)) %>%
ggplot() +
#geom_point( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Tests (log) by Country") + facet_wrap(~name_text ) + guides( color = FALSE) +
theme(
strip.background = element_blank()
, strip.text.x = element_blank()
)p_tested_people_by_country
Interpolation of Rate of Change
Plot Confirmed Curves for Select Locations
library(patchwork)
lhs_long_clean_imputed <- readRDS( "/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_clean_imputed.Rds")
dim(lhs_long_clean_imputed) #45,609 #337043
#we want the colors to be consistent by dataset too btw
#####
#USA Q30
#bp_temp <- breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=temp_df %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q30"))
#confirmed_log_y_hat_percent_change=fitted.values(bp_temp)
test <- lhs_long_clean_imputed %>%
mutate(group=paste(dataset,wikidata_id,confirmed_log_group_number)) %>%
filter(wikidata_id=="Q30") %>%
arrange(date_asdate) %>%
mutate(date_asnumeric = date_asdate %>% as.numeric() )
#rt1 <- rpart(formula = confirmed_log_y_hat_percent_change ~ date_asnumeric, data=test)
#interpolated = data.frame(date_asnumeric=min(test$date_asnumeric):max(test$date_asnumeric) )
#interpolated$confirmed_log_y_hat_percent_change_y_hat <- predict(rt1, newdata=interpolated)
#interpolated$date_asdate <- as.Date(interpolated$date_asnumeric)
p0a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("USA Q30 Confirmed") # + facet_wrap(~dataset)
p0b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3 ) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black" ) +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("USA Q30 Confirmed") # + facet_wrap(~dataset)+ ylab("Daily Percent Change Confirmed")
#p0a / p0b
#####
#China Q148
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_group_number)) %>% filter(wikidata_id=="Q148") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
p1a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("China Q148 Confirmed")
p1b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("China Q148 Confirmed")+ ylab("Daily Percent Change Confirmed")
#p1a / p1b
#India
#"Q668"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q668") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p2a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("India Q668 Confirmed")
p2b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("India Q668 Confirmed")+ ylab("Daily Percent Change Confirmed")
#p2a / p2b
#Italy
#"Q38"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p4a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Italy Q38 Confirmed")
p4b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Italy Q38 Confirmed")+ ylab("Daily Percent Change Confirmed")
#p4a / p4b
#South Korea
#"Q884"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q884") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p5a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("South Korea Q884 Confirmed")
p5b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("South Korea Q884 Confirmed") + ylab("Daily Percent Change Confirmed")
#p5a / p5b(p0a + p1a + p2a ) / (p0b + p1b + p2b )
( p4a + p5a) / ( p4b + p5b)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q1439") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p0a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Texas Q1439 Confirmed")
p0b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Texas Q1439 Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")
#Bexar
#"Q16861"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q16861") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p1a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Bexar County Q16861 Confirmed")
p1b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Bexar County Q16861 Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")
#California
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q99") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p2a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("California Q99 Confirmed")
p2b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("California Q99 Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")
#San Diego County (Q108143)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q108143") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p3a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("San Diego County (Q108143) Confirmed")
p3b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("San Diego County (Q108143) Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")
#Florida (Q812)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q812") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p4a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Florida (Q812) Confirmed")
p4b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Florida (Q812) Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")
#St. Lucie County (Q494564)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q494564") %>%
mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p5a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("St. Lucie County (Q494564) Confirmed")
p5b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("St. Lucie County (Q494564) Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")
#Not counting slopes that were interpolated out of domain fixed california's plot
( p0a + p1a ) / ( p0b + p1b )
( p2a + p3a ) / ( p2b + p3b )
( p4a + p5a) / ( p4b + p5b)
Plot Death Curves for Select Locations
Show how interpolation works on deaths
test <- lhs_long_clean_imputed %>%
mutate(group=paste(dataset,wikidata_id,deaths_log_group_number)) %>%
filter(wikidata_id=="Q30") %>%
arrange(date_asdate) %>%
mutate(date_asnumeric = date_asdate %>% as.numeric() )
p0a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("USA Q30 Deaths") # + facet_wrap(~dataset)
p0b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3 ) +
geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black" ) +
#geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("USA Q30 Deaths") + ylab("Daily Percent Change Deaths") # + facet_wrap(~dataset)
#p0a / p0b
#####
#China Q148
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_group_number)) %>% filter(wikidata_id=="Q148") %>%
mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
arrange(date_asdate) #%>%
p1a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("China Q148 Deaths")
p1b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("China Q148 Deaths")+ ylab("Daily Percent Change Deaths")
#p1a / p1b
#India
#"Q668"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q668") %>%
mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p2a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("India Q668 Deaths")
p2b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("India Q668 Deaths")+ ylab("Daily Percent Change Deaths")
#p2a / p2b
#Bexar
#"Q16861"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q16861") %>%
mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p3a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Bexar County Q16861 Deaths")
p3b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Bexar County Q16861 Deaths") + ylim(0,100)+ ylab("Daily Percent Change Deaths")
#p3a / p3b
#Italy
#"Q38"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>%
mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p4a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Italy Q38 Deaths")
p4b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Italy Q38 Deaths")+ ylab("Daily Percent Change Deaths")
#p4a / p4b
#South Korea
#"Q884"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q884") %>%
mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p5a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("South Korea Q884 Deaths")
p5b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("South Korea Q884 Deaths") + ylab("Daily Percent Change Deaths")
#p5a / p5bI need to fix this, USA has a fraction of a death
Note the discontinuity in Chin when they added those 1k deaths to the total
India deaths is broken, we shouldn’t be interpolating a rate before we think the number is greater than 0
(p0a + p1a + p2a ) / (p0b + p1b + p2b )
(p3a + p4a + p5a) / (p3b + p4b + p5b)
Plot Testing Curves for Select Locations
Show how interpolation works on tested
US is an example where we interpolated past our available data. Shouldn’t do that either!
test <- lhs_long_clean_imputed %>%
mutate(group=paste(dataset,wikidata_id,tested_people_log_group_number)) %>%
filter(wikidata_id=="Q30") %>%
arrange(date_asdate) %>%
mutate(date_asnumeric = date_asdate %>% as.numeric() )
p0a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("USA Q30 Tested") #+ facet_wrap(~dataset)
p0b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3 ) +
geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black" ) +
#geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("USA Q30 tested_people") + ylab("Daily Percent Change tested_people") # + facet_wrap(~dataset)
#p0a / p0b
#####
#China Q148
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_group_number)) %>% filter(wikidata_id=="Q148") %>%
mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
arrange(date_asdate) #%>%
p1a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("China Q148 tested_people")
p1b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("China Q148 tested_people")+ ylab("Daily Percent Change tested_people")
#p1a / p1b
#India
#"Q668"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q668") %>%
mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p2a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("India Q668 tested_people")
p2b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("India Q668 tested_people")+ ylab("Daily Percent Change tested_people")
#p2a / p2b
#Bexar
#"Q16861"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q16861") %>%
mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p3a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Bexar County Q16861 tested_people")
p3b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Bexar County Q16861 tested_people") + ylim(0,100)+ ylab("Daily Percent Change tested_people")
#p3a / p3b
#Italy
#"Q38"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>%
mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p4a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Italy Q38 tested_people")
p4b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("Italy Q38 tested_people")+ ylab("Daily Percent Change tested_people")
#p4a / p4b
#South Korea
#"Q884"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q884") %>%
mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
arrange(date_asdate) #%>%
#mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p5a <- test %>%
ggplot() +
geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("South Korea Q884 tested_people")
p5b <- test %>%
ggplot() +
geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
#geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
#geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
#geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
theme_bw() +
ggtitle("South Korea Q884 tested_people") + ylab("Daily Percent Change tested_people")
#p5a / p5bNote that China counts of testing are almost never given at the national level. We have some broken down by specific provinces but tables usually don’t have a “China” row.
(p0a + p1a + p2a ) / (p0b + p1b + p2b )
(p3a + p4a + p5a) / (p3b + p4b + p5b)
Aligning Curves by Takeoff
library(infotheo)
#Once we removed the no variance places my alignment has more mutual information than either first confirmed or 100 confirmed
mutinformation(X=df_slopes %>% dplyr::select(t,t_alligned,t_alligned_firstconfirmed,confirmed_log_y_hat_percent_change_y_hat) %>% discretize(), method="emp") #we outperformed t_alligned_100confirmed but it wasn't avail for most
#df_slopes %>% filter(abs(t_alligned)<20,abs(t_alligned_100confirmed_100)<20) %>% janitor::tabyl(t_alligned, t_alligned_100confirmed_100)
#hist(df_slopes$t_alligned - df_slopes$t_alligned_100confirmed_100, breaks=50)Plot of curves raw and then curves alligned by takeoff start
The idea here is that completely unconstrained, COVID-19 growth should follow a logistic curve, flat, upswing, constant growth, downswing, and flat again. The assumption of no unconstrained growth no longer holds because the world is reacting, but there’s still a pretty uniformly characteristic upswing across geographic units. We look for this signal of the largest first difference in the daily percent change, call that the takeoff date, and then allign all the time series with that date as 0.
p_t_alligned_firstconfirmed / p_t_alligned 
Plots by Countries
We have to add geocodes to the map placements
admin0 <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_in/admin0.Rds")
admin1 <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_in/admin1.Rds")
admin2 <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_in/admin2.Rds")
rex_clean <- function(x){ x %>% stringi::stri_trans_general("latin-ascii") %>% stringi::stri_replace_all(regex="[^A-Za-z0-9]","") %>% tolower() } #so individually, each string should be devoid of spaces,noncharacters, and nonlatin
rex_admin_function <- function(x) {
x %>%
left_join(admin0 ) %>%
left_join(admin1 ) %>%
left_join(admin2 ) %>%
mutate(gid=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',gid2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', gid1, gid0))) %>%
mutate(wikidata_id=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',wikidata_id2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', wikidata_id1, wikidata_id0))) %>%
mutate(geonameid=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',geonameid2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', geonameid1, geonameid0))) %>%
mutate(admin_level=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', 1, 0)))
}
install.packages("geofacet")# or from github:
# devtools::install_github("hafen/geofacet")
library(geofacet)
head(us_state_grid2)NAConfirmed, Deaths, Tests
By country
gadm36 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESSgadm/data_in/gadm36_gpkg/gadm36.gpkg")
gadm36_df <- gadm36 %>% as.data.frame()
countries <- gadm36_df %>% dplyr::select(gid=GID_0, name=NAME_0 ) %>% distinct()
temp1 <- df_slopes %>%
filter(!gid %>% str_detect("_") ) %>%
left_join(countries %>% dplyr::select(gid, name_text=name) ) %>%
#left_join(geonames_small ) %>%
mutate(name_text=name_text %>% as.factor()) %>%
left_join(world_countries_grid1, by=c('gid'='code_alpha3')) %>%
filter(!is.na(name))
temp2 <- temp1 %>% arrange(date_asdate) %>% filter(!duplicated(gid_geonameid_wikidata_id)) %>% mutate(name_text=name %>% as.factor())
head(world_countries_grid1)world_countries_grid1_rex <- world_countries_grid1 %>% filter(name %in% temp2$name)
dim(world_countries_grid1_rex)
grid_preview(world_countries_grid1_rex)
p_confirmed_deaths_test_by_country <- temp1 %>%
ggplot() +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat), color="red") + # #t aligned is broken for the U.S. and some countries, all infinites
geom_line( aes(x=date_asdate, y=deaths_log_y_hat), color="black") + #
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat), color="blue") + #
theme_bw() +
ggtitle("Global Covid-19: Log Tested (blue) Confirmed (red), Death (black)") +
guides( color = FALSE) +
facet_wrap(~name_text ) +
theme(
strip.background = element_blank()
, strip.text.x = element_blank()
) +
geom_text(data=temp2,x=18282+20,y=14,aes(label=name_text), size=2.5) +
ylab("Counts (log)") +
xlab("Days before/Since Takeoff in Confirmed Numbers") +
facet_geo(~name, grid =world_countries_grid1_rex) p_confirmed_deaths_test_by_country
U.S. by State
temp1 <- df_slopes %>%
filter(gid %>% str_detect("USA\\.[0-9]{1,2}_1$") ) %>%
left_join(geonames_small ) %>% mutate(name_text= name_text %>% as.factor()) %>%
left_join(us_state_grid2, by=c('name_text'='name')) %>%
filter(!is.na(name_text))
temp2 <- temp1 %>% arrange(date_asdate) %>% filter(!duplicated(gid_geonameid_wikidata_id)) %>% mutate(name_text=name_text %>% as.factor())
#t_alligned
p_confirmed_deaths_test_by_state_us <- temp1 %>%
#filter(gid=="USA") %>% #just a test
ggplot() +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat), color="red") + # #t aligned is broken for the U.S. and some countries, all infinites
geom_line( aes(x=date_asdate, y=deaths_log_y_hat), color="black") + #
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat), color="blue") + #
theme_bw() +
ggtitle("Covid-19 By U.S. State: Log Tested (blue) Confirmed (red), Death (black)") +
theme(
strip.background = element_blank()
, strip.text.x = element_blank()
) +
geom_text(data=temp2,x=18282+20,y=12,aes(label=name_text), size=4) +
ylab("Counts (log)") + xlab("Date") + #"Days before/Since Takeoff in Confirmed Numbers"
guides( color = FALSE) +
facet_geo(~code)
p_confirmed_deaths_test_by_state_us
India by State
head(india_grid2)
india_grid2_rex <- india_grid2 %>%
mutate(admin0_name_original="India") %>%
mutate(admin1_name_original=name) %>%
mutate(admin2_name_original='') %>%
mutate(admin0_name_clean=admin0_name_original %>% rex_clean()) %>%
mutate(admin1_name_clean=admin1_name_original %>% rex_clean()) %>%
mutate(admin2_name_clean=admin2_name_original %>% rex_clean()) %>%
rex_admin_function()
temp1 <- df_slopes %>%
filter(gid %>% str_detect("IND\\.[0-9]{1,2}_1$") ) %>%
left_join(india_grid2_rex ) %>%
mutate(name_text= admin1_name_original %>% as.factor()) %>% #set it to the state's name
filter(!is.na(name_text))
temp2 <- temp1 %>% arrange(date_asdate) %>% filter(!duplicated(gid_geonameid_wikidata_id)) %>% mutate(name_text=name_text %>% as.factor())
#t_alligned
p_confirmed_deaths_test_by_state_india <- temp1 %>%
#filter(gid=="USA") %>% #just a test
ggplot() +
geom_line( aes(x=date_asdate, y=confirmed_log_y_hat), color="red") + # #t aligned is broken for the U.S. and some countries, all infinites
geom_line( aes(x=date_asdate, y=deaths_log_y_hat), color="black") + #
geom_line( aes(x=date_asdate, y=tested_people_log_y_hat), color="blue") + #
theme_bw() +
ggtitle("Covid-19 By U.S. State: Log Tested (blue) Confirmed (red), Death (black)") +
theme(
strip.background = element_blank()
, strip.text.x = element_blank()
) +
geom_text(data=temp2,x=18282+50,y=7.5,aes(label=name_text), size=2.5) +
ylab("Counts (log)") + xlab("Date") + #"Days before/Since Takeoff in Confirmed Numbers"
guides( color = FALSE) +
#Error: Other than 'row' and 'col', variable names of a custom grid must begin with 'code' or 'name'
facet_geo(~code, grid = india_grid2 ) #_rex %>% filter(code, row, col, name)
p_confirmed_deaths_test_by_state_india
Tomorrow morning I think my job is to first difference all of these. How many tests, how many confirmed, how many dead each day. Find the lags and leads with the highest correlation between those three, possibly varying by country.
What this gives us is a nice normalized dataset where the task is to predict the shape of that distribution. We could try to predict time until the takeoff, the intensity of the growth at the takeoff point, the time until it returns to zero, the area under the curve, or every nook and change.
How does testing vary over this?
p_t_alligned_tested_people
Plot some specific countries
df_slopes %>% filter(!is.na(tested_people)) %>% count(wikidata_id) #292 places with testing information
df_slopes %>% filter(wikidata_id=="Q30") %>% ggplot() + geom_line(aes(x=date_asdate, y=confirmed)) + geom_line(aes(x=date_asdate, y=tested_people))
df_slopes %>% filter(wikidata_id=="Q30") %>% ggplot() + geom_line(aes(x=date_asdate, y=confirmed_log_y_hat)) + geom_line(aes(x=date_asdate, y=tested_people_log_y_hat))
df_slopes %>% filter(wikidata_id=="Q30") %>% ggplot() + geom_line(aes(x=date_asdate, y=confirmed/tested_people)) 
df_slopes %>% mutate(confirmed_over_tested_people=confirmed/tested_people) %>% pull(confirmed_over_tested_people) %>% summary() Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.04 0.07 0.71 0.13 10556.60 142175 df_slopes %>% mutate(confirmed_over_tested_people=confirmed/tested_people) %>% pull(confirmed_over_tested_people) %>% quantile(probs = seq(0, 1, 0.1), na.rm=T) #90th percential is ,23 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
1.416411e-04 1.923077e-02 3.125000e-02 4.395604e-02 5.782918e-02 7.345162e-02 9.113021e-02 1.142857e-01 1.577514e-01 2.462857e-01 1.055660e+04 df_slopes %>% ggplot() + geom_line(aes(x=t_alligned, y=confirmed/tested_people, color=wikidata_id), alpha=.2) +
gghighlight(wikidata_id %in% c('Q30','Q1439','Q668','Q884','Q16861','Q159','Q38'), unhighlighted_params=list(alpha=.5)) +
geom_smooth( aes(x=t_alligned , y=confirmed/tested_people), span = 0.05) +
xlim(-5,100) + ylim(0,.25) + theme_bw() #there are some outliars north of 10Tried to calculate with group_by(), but the calculation failed.
Falling back to ungrouped filter operation...label_key: wikidata_id
options(gghighlight_max_labels = 60)
detach(gghighlight)Error in detach(gghighlight) : invalid 'name' argumentlm1 <- lm(deaths ~ confirmed,data=lhs_long_median %>% filter(CFR<1))
lm2 <- lm(deaths ~ tested_people + confirmed,data=lhs_long_median %>% filter(CFR<1))
library(ggRandomForests); #install.packages('ggRandomForests')
rf1 <- rfsrc(deaths~ confirmed,
data=lhs_long_median %>% filter(CFR<1) %>% as.data.frame() )
gg_e <- gg_error(rf1)
gg_v <- gg_variable(rf1)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)
rf1 <- rfsrc(deaths~ confirmed + tested_people,
data=lhs_long_median %>% filter(CFR<1) %>% as.data.frame() )
gg_e <- gg_error(rf1)
gg_v <- gg_variable(rf1)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)
rf2 <- rfsrc(CFR ~ tested_people_log,
data=lhs_long_median %>%
mutate(CFR=deaths/confirmed) %>%
mutate(tested_people_log=log(tested_people+1)) %>%
filter(CFR<1) %>% as.data.frame()
)
gg_v <- gg_variable(rf2)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)
rf2 <- rfsrc(CFR ~ tested_people_log + confirmed_log,
data=lhs_long_median %>%
mutate(CFR=deaths/confirmed) %>%
mutate(tested_people_log=log(tested_people+1)) %>%
mutate(confirmed_log=log(confirmed+1)) %>%
filter(CFR<1) %>% as.data.frame()
)
gg_v <- gg_variable(rf2)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)
#Throw in time
rf3 <- rfsrc(CFR ~ positive_perc + tested_people_log,
data=lhs_long_median %>%
mutate(CFR=deaths/confirmed) %>%
mutate(positive_perc=confirmed/tested_people) %>%
mutate(tested_people_log=log(tested_people+1)) %>%
mutate(confirmed_log=log(confirmed+1)) %>%
filter(CFR<1) %>% as.data.frame()
)
gg_v <- gg_variable(rf3)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)
partial_Boston <- plot.variable(rf3,
partial=TRUE, sorted=FALSE,
show.plots = FALSE )
gg_p <- gg_partial(partial_Boston)
plot(gg_p, panel=TRUE, points = F )
copper_cts <-quantile_pts(lhs_long_median$tested_people_log, groups = 6, intervals = TRUE)
partial_coplot_Boston <- gg_partial_coplot(rf2, xvar="confirmed_log",
groups=rm_grp,
show.plots=FALSE)
summary(lhs_long_median$CFR)
lhs_long_median %>% filter(confirmed>10) %>% filter(CFR<1) %>% pull(CFR) %>% summary() #0.0297297 that's a median CFR of about 3%
lhs_long_median %>% filter(confirmed>10) %>% filter(CFR<1) %>% pull(CFR) %>% hist(breaks=50)
lhs_long_median %>% filter(confirmed>10) %>% filter(CFR<1) %>% ggplot(aes(x=CFR)) + geom_density()#This code is now depricated in favor of the tree base method above
places <- lhs_long_clean$wikidata_id %>% unique() %>% na.omit() ; length(places)
datasets <- lhs_long_clean$dataset %>% unique() %>% na.omit() ; table(dataset)
temp_list <- list()
for(p in places){
print(p)
for(d in datasets){
temp <- NULL
temp <- lhs_long_clean %>%
arrange(date_asdate) %>%
filter(dataset %in% d) %>%
filter(wikidata_id %in% p) %>%
mutate(confirmed_log=log(confirmed+1)) %>%
mutate(deaths_log=log(deaths+1)) %>%
mutate(tested_people_log=log(tested_people+1)) %>%
mutate(t= as.numeric(date_asdate) - min(as.numeric(date_asdate)) +1 ) %>% #start at 1 to make indexing easier
mutate(i= row_number() ) # actually need this
i_original <- temp$i
if( nrow(temp)==0 ) { next} #print("error");
print(p)
#bp <- breakpoints(confirmed_log ~ 1, data=temp)
bp <- NULL
lm1 <- NULL
y_hat <- NA
cdf <- NULL
try({
#if it fails fall back to just a lm
lm1 <- lm(confirmed_log ~ 1 + t, data=temp)
cdf <- data.frame(
t= 1,
confirmed_log_intercept= coef(lm1)[1],
confirmed_log_slope= coef(lm1)[2],
confirmed_log_slope_break=0
)
confirmed_log_y_hat=fitted.values(lm1)
})
try({
bp <- breakpoints(confirmed_log ~ 1 + t, data=temp, h=3)
cdf <- data.frame(
i= c(1,bp$breakpoints),
confirmed_log_intercept= coef(bp)[,1],
confirmed_log_slope= coef(bp)[,2],
confirmed_log_slope_break=1
)
confirmed_log_y_hat=fitted.values(bp)
})
try({
temp2 <- temp
temp2$confirmed_log_y_hat <- NA
temp2$confirmed_log_y_hat <- confirmed_log_y_hat
temp2 <- temp2 %>%
left_join(cdf)
q=paste0(p,"_",d)
temp2 <- temp2 %>%
expand(dataset, gid,geonameid, wikidata_id,t=min(temp$t):max(temp$t)) %>% #expand this to include all the t
left_join(temp2) %>%
mutate(date_asdate=min(date_asdate, na.rm=T)-1+t) %>% #go back interp date again
mutate(confirmed_log_slope_break = confirmed_log_slope_break %>% replace_na(0) %>% cumsum() ) %>%
fill(confirmed_log_intercept) %>%
fill(confirmed_log_slope) %>%
mutate(confirmed_log_y_hat= confirmed_log_intercept + confirmed_log_slope*t ) %>%
mutate(confirmed_log_slope_percent_change = round((exp(confirmed_log_slope)-1)*100,2))
temp_list[[as.character(q)]] <- temp2
})
#if( is.na( temp_list[[as.character(q)]]$y_hat) ) {print("error"); break}
}
}
#"13055"
temp_df <- bind_rows(temp_list)
dim(temp_df) #bigger because we're interpolating now
# install.packages("devtools")
#devtools::install_github("tidyverse/multidplyr")
library(multidplyr)
library(dplyr, warn.conflicts = FALSE)
#cluster <- new_cluster(4)
#need to supress messages
rex_function <- function(x){
temp=data.frame(confirmed_log_slope=x)
tryCatch({
#if there's an NA in x we get an error because breakpoints is one short
prediction <- breakpoints(formula=confirmed_log_slope ~ 1, data=temp, h=3) %>% fitted.values()
result=rep(NA, length(x) )
result[!is.na(x)] <-prediction
return(result)
}, error=function(e){})
return( rep(NA, length(x) ) )
}
#didn't take too long now
#library(multidplyr)
#library(dplyr, warn.conflicts = FALSE)
#cluster <- new_cluster(4)
library(tictoc)
tic()
library(strucchange)
test <- temp_df %>% head(20000)
test3 <- test %>%
group_by(wikidata_id) %>%
#partition(cluster) %>%
arrange(date_asdate) %>%
#mutate( confirmed_log_slope_y_hat = breakpoints(formula=confirmed_log_slope ~ 1, data=. ) %>% fitted.values() ) %>%
mutate( confirmed_log_slope_y_hat = rex_function(confirmed_log_slope) ) %>% #fails on the cluster
ungroup() #%>%
#collect()
toc() #48 seconds for 10k #122 seconds for 20k
saveRDS(temp_df, "/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_interpolated.Rds")---
title: "The Machine Learning for Social Science Global Subnational Covid Counts Datset"
output:
  html_notebook:
    toc: yes
    toc_float: true
    code_folding: hide
date: 
author: 
affiliation: Director, Machine Learning for Social Science Lab, Center for Peace and Security Studies, University of California San Diego
editor_options: 
  chunk_output_type: inline
---

Rex W. Douglass

<style type="text/css">
blockquote {
    padding: 10px 20px;
    margin: 0 0 20px;
    font-size: 14px;
    border-left: 5px solid #eee;
}
</style>

# Introduction

## Library Loads

```{r,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
#libraries
library(lubridate)
library(tidyverse)

#devtools::install_github("ropensci/USAboundaries")
#devtools::install_github("ropensci/USAboundariesData")
library(USAboundaries) ; #install.packages('USAboundaries')
data(state_codes)

library(tidyverse)
library(scales)
library(gghighlight)
library(lubridate)
library(R0)  # consider moving all library commands to top -- this one was in a loop below

library(WikidataR)
library(countrycode)

library(usmap) ; # install.packages('usmap')
data(statepop)
#devtools::install_github("ropensci/USAboundaries")
#devtools::install_github("ropensci/USAboundariesData")
library(USAboundaries) ; #install.packages('USAboundaries')
data(state_codes)

library(tidyverse)
library(sf)

library(jsonlite)

#This is too slow it's downloading each
library(GADMTools)
library(strucchange) ; #install.packages('strucchange')
library(tsibble)

library(patchwork)

```
## Data Loading and Cleaning

```{r, fig.width=10, fig.height=8,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

library(tsibble)

lhs_long <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long.Rds")
#dim(lhs_long) #187,305

lhs_long_clean <- lhs_long %>% 
                  dplyr::select(dataset, gid,geonameid, wikidata_id ,date_asdate, confirmed, deaths, tested_people, tested_samples) %>%
                  group_by(dataset, gid,geonameid, wikidata_id ,date_asdate) %>% #if the same source has multiple values on the same thing on the same day, or different values across unmerged obs, then take the max
                  summarise_all(max, na.rm=T) %>% 
                  ungroup() %>%
                  mutate_if(is.numeric, list(~na_if(., -Inf))) %>%
                  mutate_if(is.numeric, list(~na_if(., Inf))) %>%
                  mutate(dataset_gid_geonameid_wikidata_id= paste(dataset,gid,geonameid, wikidata_id, sep="_")  ) %>%
                  mutate(gid_geonameid_wikidata_id= paste(gid,geonameid, wikidata_id, sep="_")  ) %>%
  
                  filter(!is.na(date_asdate)) %>%
                  
                  #First drop anything that's negative. Shouldn't be any negatives.
                  filter(is.na(confirmed) | confirmed>=0) %>%
                  filter(is.na(deaths) | deaths>=0) %>%
                  filter(is.na(tested_people) | tested_people>=0) %>%
                  filter(is.na(tested_samples) | tested_samples>=0) %>%
                  
                  #Then set 0s to NA, we don't trust NAs
                  mutate(confirmed=ifelse(confirmed==0, NA, confirmed)) %>%
                  mutate(deaths=ifelse(deaths==0, NA, deaths)) %>%
                  mutate(tested_people=ifelse(tested_people==0, NA, tested_people)) %>%
                  mutate(tested_samples=ifelse(tested_samples==0, NA, tested_samples)) %>%
                
                  #Then check first differences and drop anything that doesn't weakly increase monotonically
                  group_by(dataset, gid_geonameid_wikidata_id) %>% 
                    arrange(date_asdate) %>%
                    mutate(confirmed_cummax=confirmed %>% replace_na(0) %>% 
                             cummax(), deaths_cummax=deaths%>% replace_na(0) %>% cummax(), tested_people_cummax=tested_people%>% replace_na(0) %>% cummax(), 
                             tested_samples_cummax=tested_samples%>% replace_na(0) %>% cummax()) %>%
                  ungroup() %>%  
  
                  #About 4k of these observations show a decrease from one time step to the next which suggests either an original error or a joining error
                  #We're going to straight drop those observations as a cleaning step
                  #this isn't enough because if you have consecutive mistakes it'll show a positive fd even if it's not good enough
                  filter( (is.na(confirmed) | confirmed>=confirmed_cummax) &  #never allow for a reversal
                          (is.na(deaths) | deaths>=deaths_cummax) &        #never allow for a reversal
                          (is.na(tested_people) | tested_people>=tested_people_cummax) &  #never allow for a reversal
                          (is.na(tested_samples) | tested_samples>=tested_samples_cummax)  #never allow for a reversal
                          ) %>%
  #we're going to go one step further and require either confirmed or tested to be strictly higher
  #In words, during an episode we don't believe reports mean "no new cases" just no new good reporting
  #metabiota is the worst offender here so restricting it to just that
  filter( !(
            dataset=="metabiota" & #is metabiota
            (!is.na(confirmed) &   #with a non missing confirmed
              confirmed<=confirmed_cummax ) #that is equal to or less than the cumulative max until then.
            ) #reject these
          ) %>% #metabiota is really problematic 90% of what we're doing is trying to account for it
  
  #Also reject any where deaths are greater than confirmed
  filter(is.na(confirmed) | is.na(deaths) | confirmed>=deaths) %>%
  
  #also reject any where confirmed doesn't vary 
  group_by(gid ,  geonameid ,wikidata_id) %>% #chose not to do by dataset because testing datasets might not have confirmed
    filter(var(confirmed, na.rm=T)>0) %>%
  ungroup()

  
#dim(lhs_long_clean) #281,464 #292,580 #299213 #181,563

saveRDS(lhs_long_clean,
        "/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_cleand.Rds")

```

Summary Statistics of Our Data

```{r}
print("Number of observations")
dim(lhs_long_clean)

print("Number of locations")
lhs_long_clean$wikidata_id %>% unique() %>% length() #3,373

print("Number of Days")
lhs_long_clean$date_asdate %>% unique() %>% length() #113

library(DT) #https://rstudio.github.io/DT/
lhs_long_clean %>% count(dataset) %>% arrange(-n) %>% DT::datatable(options = list(pageLength = 20, autoWidth = TRUE))

#library(gt)
#lhs_long_clean %>% count(dataset) %>% arrange(-n) %>% gt() %>% 
# tab_header(
#    title = md("Location-Days by Dataset")#,
#    #subtitle = "Number of Days with Data by Dataset"
#  ) %>%
#  fmt_missing( columns=everything(), rows=NULL,missing_text = 0)

```


# Spatial Variation in Data Availability

## Country Level Data Availability

```{r, fig.width=12, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

#gadm36 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESSgadm/data_in/gadm36_gpkg/gadm36.gpkg")
#st_layers("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg")
gadm36_levels_0 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg", layer="level0")  %>%
                  st_simplify(preserveTopology = FALSE, dTolerance =0.1) #  0.025 this is supposedly broken up by 6 levels and so should have u.s. 

#plot(gadm36_levels_0)
#dim(gadm36_levels_0_sf$sf)
#gadm_plot(gadm36_levels_0_sf)
lhs_long_place_sources <- lhs_long  %>% dplyr::select(gid,   geonameid, wikidata_id, dataset) %>% 
                           group_by(gid,  geonameid, wikidata_id) %>% count(dataset) %>%
                           group_by(gid,  geonameid, wikidata_id) %>%
                           summarise(datasets_n=n()) 

p0 <- gadm36_levels_0 %>% 
      left_join(lhs_long_place_sources %>% dplyr::select(gid=gid, datasets_n) %>%  left_join( gadm36_levels_0 %>% as.data.frame() %>% dplyr::select(gid=GID_0, NAME_0)  %>% distinct()  )
                ) %>%
      #replace_na(list(datasets_n = 0)) %>% 
      ggplot() + geom_sf(aes(fill = datasets_n)) +
      scale_fill_gradient(low="red", high="green") +
      theme_bw() 
```



```{r, fig.width=12, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p0 + ggtitle("Covid Count Data Availability at the Country Level")
```

```{r}
temp <- gadm36_levels_0 %>%
   left_join(
   lhs_long  %>% count(gid, dataset) %>% rename(GID_0=gid) #there are still dupe geonames wikidata to gid matches that need to be fixed geonameid, wikidata_id, 
) 

temp_wide <- temp %>% as.data.frame() %>% dplyr::select(Country=NAME_0, dataset, n) %>% distinct() %>% pivot_wider( id_cols = Country, names_from = dataset,
  values_from = n, values_fill = NULL, values_fn = NULL) 

temp_wide %>% DT::datatable(options = list(pageLength = 20, autoWidth = TRUE))

#install.packages('gt')
#library(gt)
#temp_wide %>% gt() %>% 
# tab_header(
#    title = md("Data Availability by Country"),
#    subtitle = "Number of Days with Data by Dataset"
#  ) %>%
#  fmt_missing( columns=everything(), rows=NULL,missing_text = 0)

```

## State/Province Level Data Availability


```{r, fig.width=12, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
gadm36_levels_1 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg", layer="level1")  %>%
  st_simplify(preserveTopology = FALSE, dTolerance =0.1) #  0.025 this is supposedly broken up by 6 levels and so should have u.s. 


p1 <- gadm36_levels_1 %>% 
  left_join(lhs_long_place_sources %>% dplyr::select(gid=gid, datasets_n) %>%  left_join( gadm36_levels_1 %>% as.data.frame() %>% dplyr::select(gid=GID_1, NAME_1) %>% distinct()  )
  ) %>%
  #replace_na(list(datasets_n = 0)) %>% 
  ggplot() + geom_sf(aes(fill = datasets_n)) +
  scale_fill_gradient(low="red", high="green") +
  theme_bw() 
```

```{r, fig.width=12, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p1
```

```{r}
temp <- gadm36_levels_1 %>%
   left_join(
   lhs_long  %>% count(gid, dataset) %>% rename(GID_1=gid) #there are still dupe geonames wikidata to gid matches that need to be fixed geonameid, wikidata_id, 
) 

temp_wide <- temp %>% as.data.frame() %>% dplyr::select(Country=NAME_0, Admin1=NAME_1, dataset, n) %>% distinct() %>% pivot_wider( id_cols = Country:Admin1, names_from = dataset,
  values_from = n, values_fill = NULL, values_fn = NULL) %>% mutate(total = rowSums(.[-c(1,2)], na.rm = T))

temp_wide %>% filter(total>0) %>% DT::datatable(options = list(pageLength = 10, autoWidth = TRUE))

```

## County District Level Data Availability

```{r, fig.width=12, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
gadm36_levels_2 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/gadm36_bycountry/gadm36_levels_gpkg/gadm36_levels.gpkg", layer="level2")  %>%
  st_simplify(preserveTopology = FALSE, dTolerance = 0.001) #  0.025 this is supposedly broken up by 6 levels and so should have u.s. I have to keep shrinking it so misisng goes to zero 

p2 <- gadm36_levels_2 %>% 
  left_join(lhs_long_place_sources %>% dplyr::select(gid=gid, datasets_n) %>%  left_join( gadm36_levels_2 %>% as.data.frame() %>% dplyr::select(gid=GID_2, NAME_2) %>% distinct()  )
  ) %>%
  #replace_na(list(datasets_n = 0)) %>% 
  ggplot() + geom_sf(aes(fill = datasets_n),lwd = 0) +
  scale_fill_gradient(low="blue", high="red") +
  theme_bw() 
```

```{r, fig.width=12, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}

p2

```


```{r}
temp <- gadm36_levels_2 %>%
   left_join(
   lhs_long  %>% count(gid, dataset) %>% rename(GID_2=gid) #there are still dupe geonames wikidata to gid matches that need to be fixed geonameid, wikidata_id, 
) 

temp_wide <- temp %>% as.data.frame() %>% dplyr::select(Country=NAME_0, Admin1=NAME_1, Admin2=NAME_2, dataset, n) %>% distinct() %>% pivot_wider( id_cols = Country:Admin2, names_from = dataset,
  values_from = n, values_fill = NULL, values_fn = NULL) %>% mutate(total = rowSums(.[-c(1,2,3)], na.rm = T))

temp_wide %>% filter(total>0) %>% DT::datatable(options = list(pageLength = 10, autoWidth = TRUE))

```

## Country Data Availability of Testing

```{r}

lhs_long_place_sources_testing <- lhs_long  %>%
                                  dplyr::select(gid,  geonameid, wikidata_id, date_asdate, confirmed, tested_people, tested_samples) %>%
                                  group_by(gid,  geonameid, wikidata_id, date_asdate) %>% 
                                  mutate_if(is.numeric, max, na.rm=T) %>%
                                  filter(!duplicated(date_asdate)) %>%
                                  ungroup() %>%
                                  mutate_if(is.numeric, list(~na_if(., -Inf))) %>%
                                  mutate_if(is.numeric, list(~na_if(., Inf))) %>%
                                  mutate(percent_of_days_without_testing=!is.na(date_asdate) & (is.na(tested_people) & is.na(tested_samples)  )) %>%
                                  
                                  group_by(gid,  geonameid, wikidata_id) %>% 
                                  summarise(
                                            percent_of_days_without_testing=sum(percent_of_days_without_testing, na.rm=T)/n()
                                            ) %>%
                                  mutate(percent_of_days_with_testing= 1-percent_of_days_without_testing) 


p0_testing <- gadm36_levels_0 %>% 
      left_join(lhs_long_place_sources_testing %>% dplyr::select(gid=gid, percent_of_days_with_testing) %>%  left_join( gadm36_levels_0 %>% as.data.frame() %>% dplyr::select(gid=GID_0, NAME_0)  %>% distinct()  )
                ) %>%
      #replace_na(list(datasets_n = 0)) %>% 
      ggplot() + geom_sf(aes(fill = percent_of_days_with_testing)) +
      scale_fill_gradient(low="red", high="green") +
      theme_bw() + ggtitle("Percent of Days with Testing counts")
```

```{r, fig.width=12, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p0_testing
```
```{r,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
p1_testing <- gadm36_levels_1 %>% 
  left_join(lhs_long_place_sources_testing %>% dplyr::select(gid=gid, percent_of_days_with_testing) %>%  left_join( gadm36_levels_1 %>% as.data.frame() %>% dplyr::select(gid=GID_1, NAME_1) %>% distinct()  )
  ) %>%
  #replace_na(list(datasets_n = 0)) %>% 
  ggplot() + geom_sf(aes(fill = percent_of_days_with_testing)) +
  scale_fill_gradient(low="red", high="green") +
  theme_bw()  + ggtitle("Percent of Days with Testing counts")
```

```{r, fig.width=12, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p1_testing
```

# Data Availability and Interpolation over Time

```{r,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

#test <- lhs_long %>% group_by(dataset, gid, geonameid, wikidata_id, date_asdate) %>% summarize(n=n())

all_na <- function(x) any(!is.na(x)) #https://intellipaat.com/community/12999/remove-columns-from-dataframe-where-all-values-are-na

#bing looks off by a day from the other ones
lhs_wide_qcode <- lhs_long %>% 
                  filter(!is.na(wikidata_id) & !is.na(date_asdate)) %>%
                  group_by(gid, geonameid, wikidata_id, date_asdate) %>%  mutate(confirmed_var=var(confirmed, na.rm=T), deaths_var=var(deaths, na.rm=T)) %>% ungroup() %>%
                  dplyr::select(dataset, gid, geonameid, wikidata_id, date_asdate,confirmed, deaths, tested_samples, tested_people) %>%
                  group_by(dataset, gid, geonameid, wikidata_id, date_asdate) %>%  summarise_if(is.numeric,max,na.rm=T) %>% ungroup() %>% #this is a hack, we should have dupes within datasets
                  pivot_wider(names_from = dataset, values_from = c(confirmed, deaths, tested_samples, tested_people) ) %>% 
                  mutate_if(is.numeric, list(~na_if(abs(.), Inf))) %>% #https://stackoverflow.com/questions/12188509/cleaning-inf-values-from-an-r-dataframe
                  select_if(all_na) 

dim(lhs_wide_qcode) #82,157 82k place/day observations

length(unique(lhs_wide_qcode$wikidata_id)) #3697 #almost 4k 

test <- lhs_wide_qcode %>% dplyr::select(starts_with("confirmed")) %>% distinct() 
#cor(test, use="pairwise.complete.obs")

lhs_long_median <- lhs_long %>% group_by(gid, geonameid, wikidata_id, date_asdate) %>% summarize_if(is.numeric, median, na.rm=T) %>% dplyr::select(-ends_with("_fd")) %>% #we take the median across observations
                   mutate(CFR=deaths/confirmed)
#dim(lhs_long_median)
#summary(lhs_long_median)
```

Here we do the actual interpolation. For each individual location-dataset, we fit a piecewise linear model over time, and then use that to average over day to day noise and interpolate between observations. Because this is in log space, the slope of that line is the day on day percent increase in the count. We finish by fitting a piecewise intercept model to those slopes to get a single daily estimate of how much the count is increasing. Datasets are able to disagree in lots of different ways and we still recover a correct rate of change.

## Interpolation of Counts

To each individual dataset-location series, we fit a piecewise linear trend. Each segment is required to have at least 3 observations, but otherwise we make no other constraints. This allows us to flexibly fit linear segments as well as tight curves. We also allow for structural breaks where the series stops, has a structural shift up or down because say a change in reporting rules, but other then otherwise continues as normal. We take the linear trend fit as our best estimate at each point. This averages over day to day noise, e.g. under reporting on the weekend. It also allows us to interpolate between observations where there is missingness in the series but we have reasonable beliefs the true measure continued linearly between the observed points.

```{r,warning=FALSE,message=FALSE,error=FALSE}

#we need to parallelize this code and see what's loudly throwing
#Error in chol.default(J12) : 
#  the leading minor of order 1 is not positive definite

dataset_places <- lhs_long_clean %>% dplyr::select(dataset_gid_geonameid_wikidata_id) %>% distinct() %>% pull(dataset_gid_geonameid_wikidata_id)
library(party)
temp_list1 <- list()
options(error = expression(NULL))
sink(NULL)
zz <- file("messages.txt", open = "wt")
sink(zz)
suppressMessages(
for(q in dataset_places  ){
      #try({
        temp <- NULL
        temp <- lhs_long_clean %>%
                filter(dataset_gid_geonameid_wikidata_id %in% q) %>%
                arrange(date_asdate) %>%

                mutate(confirmed_log=log(confirmed)) %>%
                mutate(deaths_log=log(deaths)) %>%
                mutate(tested_people_log=log(tested_people)) %>%
                mutate(tested_samples_log=log(tested_samples)) 
          
        if(nrow(temp)==0){next()}
        
        temp <- temp %>% expand(dataset, gid_geonameid_wikidata_id, gid, geonameid,wikidata_id, date_asdate=min(date_asdate):max(date_asdate) %>% as_date() ) %>% 
                 full_join(temp) %>% 
                 mutate(date_asnumeric= as.numeric(date_asdate) ) %>% 
                 arrange(date_asnumeric) %>%
                 mutate(confirmed_nonmissing_count     = (!is.na(confirmed)) %>% cumsum() ) %>%
                 mutate(deaths_nonmissing_count     = (!is.na(deaths)) %>% cumsum() ) %>%
                 mutate(tested_people_nonmissing_count     = (!is.na(tested_people)) %>% cumsum() ) %>%
                 mutate(tested_samples_nonmissing_count     = (!is.na(tested_samples)) %>% cumsum() ) %>%
        
                 arrange(-date_asnumeric) %>%
                 mutate(confirmed_nonmissing_count_reversed     = (!is.na(confirmed)) %>% cumsum() ) %>%
                 mutate(deaths_nonmissing_count_reversed     = (!is.na(deaths)) %>% cumsum() ) %>%
                 mutate(tested_people_nonmissing_count_reversed     = (!is.na(tested_people)) %>% cumsum() ) %>%
                 mutate(tested_samples_nonmissing_count_reversed     = (!is.na(tested_samples)) %>% cumsum() ) %>% 
                 arrange(date_asdate) 

        tryCatch({  
          if(sum(!is.na(temp$confirmed_log))>=3){ #need at least 3 points
            tree_confirmed <- party::mob(confirmed_log ~ 1 + date_asnumeric    | date_asnumeric , control = mob_control(minsplit = 3, alpha=.2), data = temp,  model = linearModel)  # + I(date_asnumeric^2)  
            temp$confirmed_log_y_hat <- predict( tree_confirmed, newdata=temp , type = c("response") )
            temp$confirmed_log_group_number <-  predict( tree_confirmed, newdata=temp , type = c("node"))
            temp <- temp %>% 
                    mutate(confirmed_log_y_hat       =ifelse(confirmed_nonmissing_count>0 & confirmed_nonmissing_count_reversed>0,confirmed_log_y_hat,NA)) %>% #only interpolate within the observed data, never before or after
                    mutate(confirmed_log_group_number=ifelse(confirmed_nonmissing_count>0 & confirmed_nonmissing_count_reversed>0,confirmed_log_group_number,NA)) %>% #only interpolate within the observed data, never before or after

                    group_by(confirmed_log_group_number) %>%
                    arrange(date_asnumeric) %>%
                    mutate(confirmed_log_y_hat_slope=tsibble::difference(confirmed_log_y_hat)) %>% 
                    fill(confirmed_log_y_hat_slope, .direction="up") %>% 
                    ungroup() %>%
                    mutate(confirmed_log_y_hat_percent_change = round((exp(confirmed_log_y_hat_slope)-1)*100,2))  %>%
                    mutate(confirmed_log_y_hat=ifelse(confirmed_log_y_hat<0,NA, confirmed_log_y_hat))
          }
        }, error = function(e) {}, silent=TRUE)
        
        tryCatch({  
          if(sum(!is.na(temp$deaths_log))>=3){ #need at least 3 points
            tree_deaths <- party::mob(deaths_log ~ 1 + date_asnumeric    | date_asnumeric , control = mob_control(minsplit = 3, alpha=.2), data = temp,  model = linearModel)  # + I(date_asnumeric^2)    
            temp$deaths_log_y_hat <- predict( tree_deaths, newdata=temp , type = c("response") )
            temp$deaths_log_group_number <-  predict( tree_deaths, newdata=temp , type = c("node"))
            temp <- temp %>% 
                    mutate(deaths_log_y_hat=ifelse(deaths_nonmissing_count>0 & deaths_nonmissing_count_reversed>0,deaths_log_y_hat,NA)) %>% #only interpolate within the observed data, never before
                    mutate(deaths_log_group_number=ifelse(deaths_nonmissing_count>0 & deaths_nonmissing_count_reversed>0,deaths_log_group_number,NA)) %>% #only interpolate within the observed data, never before

                    group_by(deaths_log_group_number) %>% 
                    arrange(date_asnumeric) %>% 
                    mutate(deaths_log_y_hat_slope=tsibble::difference(deaths_log_y_hat)) %>%
                    fill(deaths_log_y_hat_slope, .direction="up") %>%
                    ungroup() %>%
                    mutate(deaths_log_y_hat_percent_change = round((exp(deaths_log_y_hat_slope)-1)*100,2)) %>%
                    mutate(deaths_log_y_hat=ifelse(deaths_log_y_hat<0,NA, deaths_log_y_hat)) #reject any where the prediction is less than 1 full case
          }
        }, error = function(e) {}, silent=TRUE)
        
        tryCatch({  
          if(sum(!is.na(temp$tested_people_log))>=3){ #need at least 3 points
            tree_tested_people <- party::mob(tested_people_log ~ 1 + date_asnumeric    | date_asnumeric , control = mob_control(minsplit = 3, alpha=.2), data = temp,  model = linearModel)  # + I(date_asnumeric^2)   
            temp$tested_people_log_y_hat <- predict( tree_tested_people, newdata=temp , type = c("response") )
            temp$tested_people_log_group_number <-  predict( tree_tested_people, newdata=temp , type = c("node"))
            temp <- temp %>% 
                       mutate(tested_people_log_y_hat=ifelse(tested_people_nonmissing_count>0 & tested_people_nonmissing_count_reversed>0,tested_people_log_y_hat,NA)) %>% #only interpolate within the observed data, never before
                       mutate(tested_people_log_group_number=ifelse(tested_people_nonmissing_count>0 & tested_people_nonmissing_count_reversed>0,tested_people_log_group_number,NA)) %>% #only interpolate within the observed data, never before
              
                       group_by(tested_people_log_group_number) %>% 
                       arrange(date_asnumeric) %>%
                       mutate(tested_people_log_y_hat_slope=tsibble::difference(tested_people_log_y_hat)) %>% 
                       fill(tested_people_log_y_hat_slope, .direction="up") %>% ungroup() %>%
                       mutate(tested_people_log_y_hat_percent_change = round((exp(tested_people_log_y_hat_slope)-1)*100,2)) %>%
                       mutate(tested_people_log_y_hat=ifelse(tested_people_log_y_hat<0,NA, tested_people_log_y_hat)) #reject any where the prediction is less than 1 full case
          }
        }, error = function(e) {}, silent=TRUE)
        
        temp_list1[[q ]] <- temp
  
      #})
}
)
sink(NULL)
lhs_long_clean_imputed1 <- bind_rows(temp_list1)
dim(lhs_long_clean_imputed1) #500,786 #bigger because we're interpolating now

#rt1 <- rpart(formula = confirmed_log_y_hat_percent_change ~ date_asnumeric, data=test)
#interpolated = data.frame(date_asnumeric=min(test$date_asnumeric):max(test$date_asnumeric) )
#interpolated$confirmed_log_y_hat_percent_change_y_hat <-  predict(rt1, newdata=interpolated)
#interpolated$date_asdate <- as.Date(interpolated$date_asnumeric)

places <- lhs_long_clean %>% dplyr::select(gid_geonameid_wikidata_id) %>% distinct() %>% pull(gid_geonameid_wikidata_id)

library(rpart)
minsplit=6
minbucket=3
temp_list2 <- list()
for(q in places ){
  print(q)
  temp <- lhs_long_clean_imputed1 %>% 
                 filter(gid_geonameid_wikidata_id %in% q) %>% 
                 arrange(date_asnumeric) 
    
          
  tryCatch({  
    rt1 <- rpart::rpart(formula = confirmed_log_y_hat_percent_change ~ date_asnumeric,
                        data=temp %>% filter(!is.na(confirmed_log)), #to keep from fitting to interpolate slopes that are out of domain and usually wrong, require confirmed to not be missing or na
                        control = rpart.control(minsplit = minsplit, minbucket=minbucket))
    temp$confirmed_log_y_hat_percent_change_y_hat <-  predict(rt1, newdata=temp) #this prediction helpfully tries to impute backwards even before there are any day in any series
    #We need to nuke it if no data exist
    temp <- temp %>%
            group_by(date_asdate) %>%
            mutate( confirmed_nonmissing_count_datasets=sum(confirmed_nonmissing_count>0,na.rm=T) ) %>%
            mutate( confirmed_nonmissing_count_datasets_reversed=sum(confirmed_nonmissing_count_reversed>0,na.rm=T) ) %>%
            mutate(confirmed_log_y_hat_percent_change_y_hat=ifelse(confirmed_nonmissing_count_datasets>0 & confirmed_nonmissing_count_datasets_reversed>0,confirmed_log_y_hat_percent_change_y_hat,NA))  %>%
      ungroup()
    
  }, error = function(e) {})
  
  tryCatch({  
    rt2 <- rpart::rpart(formula = deaths_log_y_hat_percent_change ~ date_asnumeric,
                        data=temp %>% filter(!is.na(deaths_log)),
                        control = rpart.control(minsplit = minsplit, minbucket=minbucket))
    temp$deaths_log_y_hat_percent_change_y_hat <-  predict(rt2, newdata=temp)
    temp <- temp %>%
        group_by(date_asdate) %>%
        mutate( deaths_nonmissing_count_datasets=sum(deaths_nonmissing_count>0,na.rm=T) ) %>%
        mutate( deaths_nonmissing_count_datasets_reversed=sum(deaths_nonmissing_count_reversed>0,na.rm=T) ) %>%
        mutate(deaths_log_y_hat_percent_change_y_hat=ifelse(deaths_nonmissing_count_datasets>0 & deaths_nonmissing_count_datasets_reversed>0,deaths_log_y_hat_percent_change_y_hat,NA)) %>%
      ungroup()
        
  }, error = function(e) {})

  tryCatch({  
    rt3 <- rpart::rpart(formula = tested_people_log_y_hat_percent_change ~ date_asnumeric,
                    data=temp %>% filter(!is.na(tested_people_log)),
                    control = rpart.control(minsplit = minsplit, minbucket=minbucket))
    temp$tested_people_log_y_hat_percent_change_y_hat <-  predict(rt3, newdata=temp, control = rpart.control(minsplit = minsplit, minbucket=minbucket))
    temp <- temp %>%
        group_by(date_asdate) %>%
        mutate( tested_people_nonmissing_count_datasets=sum(tested_people_nonmissing_count>0,na.rm=T) ) %>%
        mutate( tested_people_nonmissing_count_datasets_reversed=sum(tested_people_nonmissing_count_reversed>0,na.rm=T) ) %>%
      
        mutate(tested_people_log_y_hat_percent_change_y_hat=ifelse(tested_people_nonmissing_count_datasets>0 & tested_people_nonmissing_count_datasets_reversed>0,tested_people_log_y_hat_percent_change_y_hat,NA))  %>%
      ungroup()
  }, error = function(e) {})
  
  temp_list2[[q ]] <- temp
}
lhs_long_clean_imputed2 <- bind_rows(temp_list2)

lhs_long_clean_imputed <- lhs_long_clean_imputed2
dim(lhs_long_clean_imputed) #629,493 #419910 #413475 #408,369 #we lose anywithout q codes here
saveRDS(lhs_long_clean_imputed, "/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_clean_imputed.Rds")

```

### Examples

#### China

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
#China
#"Q148"

test <- lhs_long_clean_imputed %>% 
        mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q148") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation0 <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("China Q148 Confirmed") + facet_wrap(~dataset   )

```

Data on China shows variation across datasets for when they start measuring. Only two observe the very beginning of the outbreak. All of the datasets show a discontinuity where reporting standards must have changed. Our method attempts to bridge that gap in 3 of the series but not two others.

```{r, fig.width=8, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p_interpolation0
```
#### Italy 

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
#Italy
#"Q38"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation1 <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("Italy Q38 Confirmed") + facet_wrap(~dataset   )

```

Italy likewise shows variation in the start of recording across datasets. There's also variation in the initial sparcity. Wikipedia is strangely missing data in the middle. Our method linearly interpolates into that space in a way in a way we probably don't want.

```{r, fig.width=8, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p_interpolation1
```

#### Italy 

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
#US
#"Q30"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q30") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation2 <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("US Q30 Confirmed") + facet_wrap(~dataset   )

```


```{r, fig.width=8, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p_interpolation2
```


#### New York State

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
#US
#"Q30"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q1384") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation3 <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("New York State Q1384 Confirmed") + facet_wrap(~dataset   )

```


```{r, fig.width=8, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p_interpolation3
```

#### New York City (by county)

nyt has it just by city, it doesn't break it down by the boroughs. But because gadm doesn't do cities we don't assign it a qcode.

usafacts has it as New York County which is New York County (Manhattan)

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
#Brooklyn (Q18419)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q855974") %>%  #no hits?
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation4a <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("Bronx County (Q855974)") + facet_wrap(~dataset   ) + guides( color = FALSE)

#Queens County, New York Q5142559
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q5142559") %>%  #no hits?
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation4b <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("Queens County, New York Q5142559") + facet_wrap(~dataset   ) + guides( color = FALSE)

#Brooklyn is aparently Kings County Q11980692
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q11980692") %>%  #no hits?
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation4c <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("Brooklyn is Kings County Q11980692") + facet_wrap(~dataset   ) + guides( color = FALSE)

# Manhattan is New York County Q500416
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q500416") %>%  #no hits?
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation4d <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("Manhattan is New York County Q500416") + facet_wrap(~dataset   ) + guides( color = FALSE)

#Staten Island is richmond county Q11997784
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q11997784") %>%  #no hits?
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )

p_interpolation4e <- test %>%
                    ggplot() + 
                       geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                       geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                       #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                       theme_bw() +
                       ggtitle("Staten Island  Richmond County Q11997784") + facet_wrap(~dataset   ) + guides( color = FALSE)

```

Note that CSSE is conflating all of New York City with Manhattan/New York County, Bing is too I think

```{r, fig.width=8, fig.height=6,warning=FALSE,message=FALSE,error=FALSE}
p_interpolation4a + p_interpolation4b + p_interpolation4c + p_interpolation4d + p_interpolation4e
```



## Plots by Countries

```{r}
library(data.table)
geonames <- fread("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/allCountries.txt", sep="\t") %>% mutate_if(is.numeric,as.character) %>% mutate_if(is.factor,as.character)
dim(geonames) #12,006,426

geonames_small <- geonames %>% dplyr::select(geonameid=V1, name_text=V2) %>% inner_join( lhs_long_clean_imputed %>% dplyr::select(geonameid) %>% distinct() )
dim(geonames_small)

```

### Confirmed
```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
p_confirmed_by_country <- lhs_long_clean_imputed %>%
                          filter(!gid %>% str_detect("_") ) %>%
                          left_join(geonames_small  ) %>% mutate(name_text %>% as.factor()) %>%
                          mutate(group=paste(gid_geonameid_wikidata_id,confirmed_log_group_number)) %>%
                          ggplot() + 
                             #geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
                             geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
                             #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                             theme_bw() +
                             ggtitle("Confirmed (log) by Country") + facet_wrap(~name_text   ) + guides( color = FALSE) +
                            theme(
                              strip.background = element_blank()
                            ,  strip.text.x = element_blank()
                            )
```

```{r}
p_confirmed_by_country
```

### Deaths

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
p_deaths_by_country <- lhs_long_clean_imputed %>%
                          filter(!gid %>% str_detect("_") ) %>%
                          left_join(geonames_small  ) %>% mutate(name_text %>% as.factor()) %>%
                          mutate(group=paste(gid_geonameid_wikidata_id,confirmed_log_group_number)) %>%
                          ggplot() + 
                             #geom_point( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset), alpha=.3) +
                             geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
                             #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                             theme_bw() +
                             ggtitle("Deaths (log) by Country") + facet_wrap(~name_text   ) + guides( color = FALSE) +
                            theme(
                              strip.background = element_blank()
                            ,  strip.text.x = element_blank()
                            )
```

```{r, fig.width=16, fig.height=12}
p_deaths_by_country
```

### Tests

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
p_tested_people_by_country <- lhs_long_clean_imputed %>%
                          filter(!gid %>% str_detect("_") ) %>%
                          left_join(geonames_small  ) %>% mutate(name_text %>% as.factor()) %>%
                          mutate(group=paste(gid_geonameid_wikidata_id,confirmed_log_group_number)) %>%
                          ggplot() + 
                             #geom_point( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset), alpha=.3) +
                             geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
                             #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
                             theme_bw() +
                             ggtitle("Tests (log) by Country") + facet_wrap(~name_text   ) + guides( color = FALSE) +
                            theme(
                              strip.background = element_blank()
                            ,  strip.text.x = element_blank()
                            )
```

```{r, fig.width=16, fig.height=12}
p_tested_people_by_country
```


## Interpolation of Rate of Change

## Plot Confirmed Curves for Select Locations

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

library(patchwork)
lhs_long_clean_imputed <- readRDS( "/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_clean_imputed.Rds")
dim(lhs_long_clean_imputed) #45,609 #337043

#we want the colors to be consistent by dataset too btw

#####
#USA Q30

#bp_temp <- breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=temp_df %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q30"))
#confirmed_log_y_hat_percent_change=fitted.values(bp_temp)

test <- lhs_long_clean_imputed %>%
        mutate(group=paste(dataset,wikidata_id,confirmed_log_group_number)) %>%
        filter(wikidata_id=="Q30") %>% 
        arrange(date_asdate)  %>%
        mutate(date_asnumeric = date_asdate %>% as.numeric() )

#rt1 <- rpart(formula = confirmed_log_y_hat_percent_change ~ date_asnumeric, data=test)
#interpolated = data.frame(date_asnumeric=min(test$date_asnumeric):max(test$date_asnumeric) )
#interpolated$confirmed_log_y_hat_percent_change_y_hat <-  predict(rt1, newdata=interpolated)
#interpolated$date_asdate <- as.Date(interpolated$date_asnumeric)

p0a <- test %>%
        ggplot() + 
           geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
           geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
           #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
           theme_bw() +
           ggtitle("USA Q30 Confirmed") # + facet_wrap(~dataset) 

p0b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3 ) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black"  ) +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("USA Q30 Confirmed") # + facet_wrap(~dataset)+ ylab("Daily Percent Change Confirmed")

#p0a / p0b



#####
#China Q148

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_group_number)) %>% filter(wikidata_id=="Q148") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%

p1a <- test %>% 
ggplot() + 
   geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("China Q148 Confirmed")

p1b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("China Q148 Confirmed")+ ylab("Daily Percent Change Confirmed")
   

#p1a / p1b

#India
#"Q668"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q668") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p2a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("India Q668 Confirmed")

p2b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("India Q668 Confirmed")+ ylab("Daily Percent Change Confirmed")
 
  
#p2a / p2b



#Italy
#"Q38"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p4a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("Italy Q38 Confirmed")

p4b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("Italy Q38 Confirmed")+ ylab("Daily Percent Change Confirmed")
   
    
#p4a / p4b



#South Korea
#"Q884"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q884") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p5a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("South Korea Q884  Confirmed")

p5b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("South Korea Q884 Confirmed") + ylab("Daily Percent Change Confirmed")
   
    
#p5a / p5b
```



```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
(p0a + p1a + p2a ) / (p0b + p1b + p2b )
```

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
( p4a + p5a) / ( p4b + p5b)
```

```{r}


test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q1439") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p0a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("Texas Q1439 Confirmed")

p0b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("Texas Q1439 Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")

#Bexar
#"Q16861"
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q16861") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p1a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("Bexar County Q16861 Confirmed")
p1b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("Bexar County Q16861 Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")

#California
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q99") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p2a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("California Q99 Confirmed")
p2b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("California Q99 Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")

#San Diego County (Q108143)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q108143") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p3a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("San Diego County (Q108143) Confirmed")
p3b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("San Diego County (Q108143) Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")

#Florida (Q812)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q812") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p4a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("Florida (Q812) Confirmed")
p4b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("Florida (Q812) Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")


#St. Lucie County (Q494564)
test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,confirmed_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q494564") %>% 
        mutate(group=paste(dataset, wikidata_id,confirmed_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( confirmed_log_y_hat_percent_change = breakpoints(formula=confirmed_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )
p5a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("St. Lucie County (Q494564) Confirmed")
p5b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=confirmed_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=confirmed_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=confirmed_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("St. Lucie County (Q494564) Confirmed") + ylim(0,100)+ ylab("Daily Percent Change Confirmed")


```

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
#Not counting slopes that were interpolated out of domain fixed california's plot
( p0a + p1a ) / ( p0b + p1b )
```

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
( p2a + p3a ) / ( p2b + p3b )
```

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
( p4a + p5a) / ( p4b + p5b)
```


## Plot Death Curves for Select Locations

Show how interpolation works on deaths

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

test <- lhs_long_clean_imputed %>%
        mutate(group=paste(dataset,wikidata_id,deaths_log_group_number)) %>%
        filter(wikidata_id=="Q30") %>% 
        arrange(date_asdate)  %>%
        mutate(date_asnumeric = date_asdate %>% as.numeric() )

p0a <- test %>%
        ggplot() + 
           geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
           geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
           #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
           theme_bw() +
           ggtitle("USA Q30 Deaths") # + facet_wrap(~dataset) 

p0b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3 ) +
         geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black"  ) +
         #geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("USA Q30 Deaths") + ylab("Daily Percent Change Deaths") # + facet_wrap(~dataset)

#p0a / p0b


#####
#China Q148

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_group_number)) %>% filter(wikidata_id=="Q148") %>% 
        mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
        arrange(date_asdate) #%>%

p1a <- test %>% 
ggplot() + 
   geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("China Q148 Deaths")

p1b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("China Q148 Deaths")+ ylab("Daily Percent Change Deaths")
   

#p1a / p1b

#India
#"Q668"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q668") %>% 
        mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p2a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("India Q668 Deaths")

p2b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("India Q668 Deaths")+ ylab("Daily Percent Change Deaths")
 
  
#p2a / p2b

#Bexar
#"Q16861"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q16861") %>% 
        mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p3a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("Bexar County Q16861 Deaths")

p3b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("Bexar County Q16861 Deaths") + ylim(0,100)+ ylab("Daily Percent Change Deaths")
   
#p3a / p3b



#Italy
#"Q38"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>% 
        mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p4a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("Italy Q38 Deaths")

p4b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("Italy Q38 Deaths")+ ylab("Daily Percent Change Deaths")
   
    
#p4a / p4b



#South Korea
#"Q884"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,deaths_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q884") %>% 
        mutate(group=paste(dataset, wikidata_id,deaths_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( deaths_log_y_hat_percent_change = breakpoints(formula=deaths_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p5a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=deaths_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("South Korea Q884  Deaths")

p5b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=deaths_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=deaths_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=deaths_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=deaths_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("South Korea Q884 Deaths") + ylab("Daily Percent Change Deaths")
   
    
#p5a / p5b
```

I need to fix this, USA has a fraction of a death

Note the discontinuity in Chin when they added those 1k deaths to the total

India deaths is broken, we shouldn't be interpolating a rate before we think the number is greater than 0

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
(p0a + p1a + p2a ) / (p0b + p1b + p2b )
```

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
(p3a + p4a + p5a) / (p3b + p4b + p5b)
```



## Plot Testing Curves for Select Locations

Show how interpolation works on tested

US is an example where we interpolated past our available data. Shouldn't do that either!
```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

test <- lhs_long_clean_imputed %>%
        mutate(group=paste(dataset,wikidata_id,tested_people_log_group_number)) %>%
        filter(wikidata_id=="Q30") %>% 
        arrange(date_asdate)  %>%
        mutate(date_asnumeric = date_asdate %>% as.numeric() )

p0a <- test %>%
        ggplot() + 
           geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
           geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
           #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
           theme_bw() +
           ggtitle("USA Q30 Tested")  #+ facet_wrap(~dataset) 

p0b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3 ) +
         geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black"  ) +
         #geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("USA Q30 tested_people") + ylab("Daily Percent Change tested_people") # + facet_wrap(~dataset)

#p0a / p0b


#####
#China Q148

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_group_number)) %>% filter(wikidata_id=="Q148") %>% 
        mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
        arrange(date_asdate) #%>%

p1a <- test %>% 
ggplot() + 
   geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("China Q148 tested_people")

p1b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("China Q148 tested_people")+ ylab("Daily Percent Change tested_people")
   

#p1a / p1b

#India
#"Q668"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q668") %>% 
        mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p2a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("India Q668 tested_people")

p2b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("India Q668 tested_people")+ ylab("Daily Percent Change tested_people")
 
  
#p2a / p2b

#Bexar
#"Q16861"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q16861") %>% 
        mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p3a <- test %>%
      ggplot() + 
         geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
         geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
         ggtitle("Bexar County Q16861 tested_people")

p3b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("Bexar County Q16861 tested_people") + ylim(0,100)+ ylab("Daily Percent Change tested_people")
   
#p3a / p3b



#Italy
#"Q38"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q38") %>% 
        mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p4a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("Italy Q38 tested_people")

p4b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("Italy Q38 tested_people")+ ylab("Daily Percent Change tested_people")
   
    
#p4a / p4b



#South Korea
#"Q884"

test <- lhs_long_clean_imputed %>% mutate(group=paste(dataset,wikidata_id,tested_people_log_y_hat_percent_change)) %>% filter(wikidata_id=="Q884") %>% 
        mutate(group=paste(dataset, wikidata_id,tested_people_log_group_number)) %>%
        arrange(date_asdate) #%>%
        #mutate( tested_people_log_y_hat_percent_change = breakpoints(formula=tested_people_log_y_hat_percent_change ~ 1, data=. ) %>% fitted.values() )


p5a <- test %>%
ggplot() + 
   geom_point( aes(x=date_asdate, y=tested_people_log, color=dataset), alpha=.3) +
   geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("South Korea Q884  tested_people")

p5b <- test %>%
       ggplot() + 
         geom_point( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change, color=dataset), alpha=.3) +
         geom_line(aes(x=date_asdate , y=tested_people_log_y_hat_percent_change_y_hat ), alpha=1, color="black") +
         #geom_smooth( aes(x=date_asdate , y=tested_people_log_y_hat_percent_change), alpha=.3, span=.05) + #, span=.05
         #geom_line( aes(x=date_asdate, y=tested_people_log_y_hat, color=dataset, group=group)) + #
         #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
         theme_bw() +
   ggtitle("South Korea Q884 tested_people") + ylab("Daily Percent Change tested_people")
   
    
#p5a / p5b
```


Note that China counts of testing are almost never given at the national level. We have some broken down by specific provinces but tables usually don't have a "China" row.

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
(p0a + p1a + p2a ) / (p0b + p1b + p2b )
```

```{r, fig.width=12, fig.height=6 ,warning=FALSE,message=FALSE,error=FALSE}
(p3a + p4a + p5a) / (p3b + p4b + p5b)
```


# Aligning Curves by Takeoff

```{r, echo=FALSE,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

library(data.table)
geonames <- fread("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/allCountries.txt", sep="\t") %>% mutate_if(is.numeric,as.character) %>% mutate_if(is.factor,as.character)
dim(geonames) #12,006,426

geonames_small <- geonames %>% dplyr::select(geonameid=V1, name_text=V2) %>% inner_join( lhs_long_clean_imputed %>% dplyr::select(geonameid) %>% distinct() )
dim(geonames_small)

df_slopes <- lhs_long_clean_imputed %>% 
             group_by(gid_geonameid_wikidata_id, gid, geonameid, wikidata_id, date_asdate) %>% 
             summarise(
                    confirmed=median(confirmed, na.rm=T),
                    deaths=median(deaths, na.rm=T),
                    tested_people=median(tested_people, na.rm=T),
                    tested_samples=median(tested_samples, na.rm=T),
                    
                    confirmed_log_y_hat=median(confirmed_log_y_hat, na.rm=T),
                    deaths_log_y_hat=median(deaths_log_y_hat, na.rm=T),
                    tested_people_log_y_hat=median(tested_people_log_y_hat, na.rm=T),

                    confirmed_log_y_hat_percent_change=median(confirmed_log_y_hat_percent_change, na.rm=T),
                    deaths_log_y_hat_percent_change=median(deaths_log_y_hat_percent_change, na.rm=T),
                    tested_people_log_y_hat_percent_change=median(tested_people_log_y_hat_percent_change, na.rm=T),

                    confirmed_log_y_hat_percent_change_y_hat=median(confirmed_log_y_hat_percent_change_y_hat, na.rm=T),
                    deaths_log_y_hat_percent_change_y_hat=median(deaths_log_y_hat_percent_change_y_hat, na.rm=T),
                    tested_people_log_y_hat_percent_change_y_hat=median(tested_people_log_y_hat_percent_change_y_hat, na.rm=T),
                                        
                    #tested_samples_log_y_hat=median(tested_samples_log_y_hat, na.rm=T), #haven't written the interp for this yet
                    ) %>%
             #ungroup() %>%
             left_join(geonames_small) %>%
             group_by(gid_geonameid_wikidata_id) %>% 
             arrange(date_asdate) %>% 
             mutate(t = row_number()) %>%
             mutate(confirmed_log_y_hat_percent_change_y_hat_fd=tsibble::difference(confirmed_log_y_hat_percent_change_y_hat)) %>%
  
             #So there are ones that start low and build up
             mutate(t_alligned_maxfd =  confirmed_log_y_hat_percent_change_y_hat_fd==max(confirmed_log_y_hat_percent_change_y_hat_fd,na.rm=T) ) %>% 
             #but there are other ones that go from zero to high and those aren't getting caught by the top above
             mutate(t_alligned_max_at_0 =  confirmed_log_y_hat_percent_change_y_hat==max(confirmed_log_y_hat_percent_change_y_hat,na.rm=T) & t==1 ) %>% 

             mutate(t_alligned = ifelse(t_alligned_max_at_0 | (t_alligned_maxfd & max(t_alligned_max_at_0, na.rm=T)==F) , t, NA) ) %>% 
             mutate(t_alligned = max(t_alligned, na.rm=T) ) %>%
             mutate(t_alligned = t-max(t_alligned, na.rm=T) ) %>%

             mutate(t_alligned_firstconfirmed = ifelse(confirmed==min(confirmed, na.rm=T) , t, NA) ) %>% 
             mutate(t_alligned_firstconfirmed = max(t_alligned_firstconfirmed, na.rm=T) ) %>%
             mutate(t_alligned_firstconfirmed = t-max(t_alligned_firstconfirmed, na.rm=T) ) %>%

             #this throws a bunch of infinite missing because of countries that haven't yet hit 100
             #mutate(t_alligned_100confirmed_100 = ifelse(confirmed>=100  , t, NA) ) %>% 
             #mutate(t_alligned_100confirmed_100 = ifelse(t_alligned_100confirmed_100==min(t_alligned_100confirmed_100, na.rm=T) , t, NA) ) %>% 
             #mutate(t_alligned_100confirmed = t-max(t_alligned_100confirmed_100, na.rm=T) ) %>%
             #dplyr::select(-t_alligned_100confirmed_100) %>%
             ungroup()
```

```{r, eval=F}

library(infotheo)
#Once we removed the no variance places my alignment has more mutual information than either first confirmed or 100 confirmed
mutinformation(X=df_slopes %>% dplyr::select(t,t_alligned,t_alligned_firstconfirmed,confirmed_log_y_hat_percent_change_y_hat) %>% discretize(), method="emp") #we outperformed t_alligned_100confirmed but it wasn't avail for most

#df_slopes %>% filter(abs(t_alligned)<20,abs(t_alligned_100confirmed_100)<20) %>% janitor::tabyl(t_alligned, t_alligned_100confirmed_100)
#hist(df_slopes$t_alligned - df_slopes$t_alligned_100confirmed_100, breaks=50)
  
```

Plot of curves raw and then curves alligned by takeoff start

```{r, fig.width=12, fig.height=12, echo=FALSE,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

#df_slopes %>% mutate(group=paste(wikidata_id)) %>%
#              ggplot() + 
#              geom_line( aes(x=t_alligned , y=confirmed_log_y_hat_percent_change_y_hat, group=wikidata_id), alpha=.05, color="blue") +
#              theme_bw() +
#              ggtitle("Interpolated log count by Alligned T") + ylim(0,100)


p_t_alligned_firstconfirmed <-  df_slopes %>% mutate(group=paste(wikidata_id)) %>% 
                               ggplot() + 
                               geom_line( aes(x=t_alligned_firstconfirmed , y=confirmed_log_y_hat_percent_change_y_hat, group=group, color=wikidata_id), alpha=1) +
                               theme_bw() +
                               ggtitle("Interpolated log count by T") + ylim(0,100) +
                               gghighlight(wikidata_id %in% c('Q30','Q1439','Q668','Q884','Q16861','Q159','Q38'), unhighlighted_params=list(alpha=.1)) +
                                 geom_smooth( aes(x=t_alligned_firstconfirmed , y=confirmed_log_y_hat_percent_change_y_hat), span = 0.05)  + xlim(-50,100)

#p_t_alligned_firstconfirmed


p_t_alligned <- df_slopes %>% mutate(group=paste(wikidata_id)) %>% 
               ggplot() + 
               geom_line( aes(x=t_alligned , y=confirmed_log_y_hat_percent_change_y_hat, group=group, color=wikidata_id), alpha=1) +
               theme_bw() +
               ggtitle("Interpolated log count by Alligned T") + ylim(0,100) +
               gghighlight(wikidata_id %in% c('Q30','Q1439','Q668','Q884','Q16861','Q159','Q38'), unhighlighted_params=list(alpha=.1)) +
               geom_smooth( aes(x=t_alligned , y=confirmed_log_y_hat_percent_change_y_hat), span = 0.05) + xlim(-50,100)

#p_t_alligned
#df_slopes %>% mutate(group=paste(wikidata_id)) %>% 
#ggplot() + 
#   geom_line( aes(x=t_alligned , y=confirmed_log_y_hat_percent_change_y_hat, group=group, color=wikidata_id), alpha=1) +
#   theme_bw() +
#   ggtitle("Interpolated log count by Alligned T") + ylim(0,100)  + xlim(0,100) +
#   gghighlight(wikidata_id %in% c('Q30','Q1439','Q668','Q884','Q16861','Q159','Q38'), unhighlighted_params=list(alpha=.1)) +
#   geom_smooth( aes(x=t_alligned , y=confirmed_log_y_hat_percent_change_y_hat), span = 0.05) 



```

The idea here is that completely unconstrained, COVID-19 growth should follow a logistic curve, flat, upswing, constant growth, downswing, and flat again. The assumption of no unconstrained growth no longer holds because the world is reacting, but there's still a pretty uniformly characteristic upswing across geographic units. We look for this signal of the largest first difference in the daily percent change, call that the takeoff date, and then allign all the time series with that date as 0.

```{r, fig.width=12, fig.height=12,warning=FALSE,message=FALSE,error=FALSE}
p_t_alligned_firstconfirmed  / p_t_alligned 
```




## Plots by Countries

We have to add geocodes to the map placements

```{r}


admin0 <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_in/admin0.Rds")
admin1 <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_in/admin1.Rds")
admin2 <- readRDS("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_in/admin2.Rds")

rex_clean <- function(x){ x %>% stringi::stri_trans_general("latin-ascii") %>% stringi::stri_replace_all(regex="[^A-Za-z0-9]","") %>% tolower() } #so individually, each string should be devoid of spaces,noncharacters, and nonlatin

rex_admin_function <- function(x) {
  x %>% 
  left_join(admin0 ) %>%
  left_join(admin1 )  %>%
  left_join(admin2 ) %>%
  mutate(gid=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',gid2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', gid1, gid0))) %>%
  mutate(wikidata_id=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',wikidata_id2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', wikidata_id1, wikidata_id0))) %>%
  mutate(geonameid=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',geonameid2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', geonameid1, geonameid0))) %>%
  mutate(admin_level=ifelse(!is.na(admin2_name_clean) & admin2_name_clean!='',2,ifelse(!is.na(admin1_name_clean) & admin1_name_clean!='', 1, 0)))
}

```

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}
install.packages("geofacet")
# or from github:
# devtools::install_github("hafen/geofacet")
library(geofacet)
head(us_state_grid2)

```

### Confirmed, Deaths, Tests

By country 

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

gadm36 = st_read("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESSgadm/data_in/gadm36_gpkg/gadm36.gpkg")
gadm36_df <- gadm36 %>% as.data.frame()
countries <- gadm36_df %>% dplyr::select(gid=GID_0, name=NAME_0 ) %>% distinct()

temp1 <- df_slopes %>%
          filter(!gid %>% str_detect("_") ) %>%
          left_join(countries %>% dplyr::select(gid, name_text=name) ) %>% 
          #left_join(geonames_small  ) %>% 
  
          mutate(name_text=name_text %>% as.factor()) %>%
          left_join(world_countries_grid1, by=c('gid'='code_alpha3')) %>%
          filter(!is.na(name))
temp2 <- temp1 %>% arrange(date_asdate) %>% filter(!duplicated(gid_geonameid_wikidata_id)) %>% mutate(name_text=name %>% as.factor())

head(world_countries_grid1)
world_countries_grid1_rex <- world_countries_grid1 %>% filter(name %in% temp2$name)
dim(world_countries_grid1_rex)
grid_preview(world_countries_grid1_rex)

p_confirmed_deaths_test_by_country <- temp1 %>% 
                                      ggplot() + 
                                         geom_line( aes(x=date_asdate, y=confirmed_log_y_hat), color="red") + # #t aligned is broken for the U.S. and some countries, all infinites
                                         geom_line( aes(x=date_asdate, y=deaths_log_y_hat), color="black") + #
                                         geom_line( aes(x=date_asdate, y=tested_people_log_y_hat), color="blue") + #
                                         theme_bw() +
                                         ggtitle("Global Covid-19: Log Tested (blue) Confirmed (red), Death (black)") +
                                        guides( color = FALSE) +
                                        facet_wrap(~name_text   ) +
                                        theme(
                                          strip.background = element_blank()
                                        ,  strip.text.x = element_blank()
                                        ) +
                                        geom_text(data=temp2,x=18282+20,y=14,aes(label=name_text), size=2.5) +
                                        ylab("Counts (log)") +
                                        xlab("Days before/Since Takeoff in Confirmed Numbers") +
                                        facet_geo(~name, grid =world_countries_grid1_rex) 
```

```{r, fig.width=16, fig.height=12}
p_confirmed_deaths_test_by_country
```

U.S. by State

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

temp1 <- df_slopes %>%
          filter(gid %>% str_detect("USA\\.[0-9]{1,2}_1$") ) %>%
          left_join(geonames_small  ) %>% mutate(name_text= name_text %>% as.factor()) %>%
          left_join(us_state_grid2, by=c('name_text'='name')) %>% 
          filter(!is.na(name_text))
temp2 <- temp1 %>% arrange(date_asdate) %>% filter(!duplicated(gid_geonameid_wikidata_id)) %>% mutate(name_text=name_text %>% as.factor())

#t_alligned
p_confirmed_deaths_test_by_state_us <- temp1 %>% 
                                        #filter(gid=="USA") %>% #just a test
                                        ggplot() + 
                                           geom_line( aes(x=date_asdate, y=confirmed_log_y_hat), color="red") + # #t aligned is broken for the U.S. and some countries, all infinites
                                           geom_line( aes(x=date_asdate, y=deaths_log_y_hat), color="black") + #
                                           geom_line( aes(x=date_asdate, y=tested_people_log_y_hat), color="blue") + #
                                           theme_bw() +
                                           ggtitle("Covid-19 By U.S. State: Log Tested (blue) Confirmed (red), Death (black)") +
                                          theme(
                                            strip.background = element_blank()
                                          ,  strip.text.x = element_blank()
                                          ) +
                                          geom_text(data=temp2,x=18282+20,y=12,aes(label=name_text), size=4) +
                                          ylab("Counts (log)") + xlab("Date") + #"Days before/Since Takeoff in Confirmed Numbers"
                                          guides( color = FALSE) +
                                          facet_geo(~code) 

 
```

```{r, fig.width=16, fig.height=12}
p_confirmed_deaths_test_by_state_us
```

India by State

```{r, fig.width=16, fig.height=12,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

head(india_grid2)

india_grid2_rex <- india_grid2 %>%
                    mutate(admin0_name_original="India") %>%
                    mutate(admin1_name_original=name) %>%
                    mutate(admin2_name_original='')  %>%
    
                    mutate(admin0_name_clean=admin0_name_original %>% rex_clean()) %>%
                    mutate(admin1_name_clean=admin1_name_original %>% rex_clean()) %>%
                    mutate(admin2_name_clean=admin2_name_original %>% rex_clean()) %>%
                    rex_admin_function()

temp1 <- df_slopes %>%
          filter(gid %>% str_detect("IND\\.[0-9]{1,2}_1$") ) %>%
          left_join(india_grid2_rex  ) %>% 
          mutate(name_text= admin1_name_original %>% as.factor()) %>% #set it to the state's name
          filter(!is.na(name_text))
temp2 <- temp1 %>% arrange(date_asdate) %>% filter(!duplicated(gid_geonameid_wikidata_id)) %>% mutate(name_text=name_text %>% as.factor())

#t_alligned
p_confirmed_deaths_test_by_state_india <- temp1 %>% 
                                          #filter(gid=="USA") %>% #just a test
                                          ggplot() + 
                                             geom_line( aes(x=date_asdate, y=confirmed_log_y_hat), color="red") + # #t aligned is broken for the U.S. and some countries, all infinites
                                             geom_line( aes(x=date_asdate, y=deaths_log_y_hat), color="black") + #
                                             geom_line( aes(x=date_asdate, y=tested_people_log_y_hat), color="blue") + #
                                             theme_bw() +
                                             ggtitle("Covid-19 By U.S. State: Log Tested (blue) Confirmed (red), Death (black)") +
                                            theme(
                                              strip.background = element_blank()
                                            ,  strip.text.x = element_blank()
                                            ) +
                                            geom_text(data=temp2,x=18282+50,y=7.5,aes(label=name_text), size=2.5) +
                                            ylab("Counts (log)") + xlab("Date") + #"Days before/Since Takeoff in Confirmed Numbers"
                                            guides( color = FALSE) +
                                            #Error: Other than 'row' and 'col', variable names of a custom grid must begin with 'code' or 'name'
                                            facet_geo(~code, grid = india_grid2 )  #_rex %>% filter(code, row, col, name)

 
```

```{r, fig.width=16, fig.height=12}
p_confirmed_deaths_test_by_state_india
```






Tomorrow morning I think my job is to first difference all of these. How many tests, how many confirmed, how many dead each day. Find the lags and leads with the highest correlation between those three, possibly varying by country.







What this gives us is a nice normalized dataset where the task is to predict the shape of that distribution. We could try to predict time until the takeoff, the intensity of the growth at the takeoff point, the time until it returns to zero, the area under the curve, or every nook and change.


How does testing vary over this?


```{r, fig.width=12, fig.height=12, echo=FALSE,warning=FALSE,message=FALSE,error=FALSE, results='hide'}

p_t_alligned_tested_people <- df_slopes %>% mutate(group=paste(wikidata_id)) %>% 
                             ggplot() + 
                             geom_line( aes(x=t_alligned , y=tested_people_log_y_hat_percent_change_y_hat, group=group, color=wikidata_id), alpha=1) +
                             theme_bw() +
                             ggtitle("Percent Day on Day Increase in Testing by Alligned T") + ylim(0,100) +
                             gghighlight(wikidata_id %in% c('Q30','Q1439','Q668','Q884','Q16861','Q159','Q38'), unhighlighted_params=list(alpha=.5)) +
                             geom_smooth( aes(x=t_alligned , y=confirmed_log_y_hat_percent_change_y_hat), span = 0.05) + xlim(-50,100) 

```

```{r, fig.width=12, fig.height=12,warning=FALSE,message=FALSE,error=FALSE}
p_t_alligned_tested_people
```


Plot some specific countries


```{r}

df_slopes %>% filter(!is.na(tested_people)) %>% count(wikidata_id) #292 places with testing information

df_slopes %>% filter(wikidata_id=="Q30") %>% ggplot() + geom_line(aes(x=date_asdate, y=confirmed)) + geom_line(aes(x=date_asdate, y=tested_people))

df_slopes %>% filter(wikidata_id=="Q30") %>% ggplot() + geom_line(aes(x=date_asdate, y=confirmed_log_y_hat)) + geom_line(aes(x=date_asdate, y=tested_people_log_y_hat))

df_slopes %>% filter(wikidata_id=="Q30") %>% ggplot() + geom_line(aes(x=date_asdate, y=confirmed/tested_people)) 


df_slopes %>% mutate(confirmed_over_tested_people=confirmed/tested_people) %>% pull(confirmed_over_tested_people) %>% summary()
df_slopes %>% mutate(confirmed_over_tested_people=confirmed/tested_people) %>% pull(confirmed_over_tested_people) %>% quantile(probs = seq(0, 1, 0.1), na.rm=T) #90th percential is ,23

df_slopes %>% ggplot() + geom_line(aes(x=t_alligned, y=confirmed/tested_people, color=wikidata_id), alpha=.2) + 
                             gghighlight(wikidata_id %in% c('Q30','Q1439','Q668','Q884','Q16861','Q159','Q38'), unhighlighted_params=list(alpha=.5)) +
                             geom_smooth( aes(x=t_alligned , y=confirmed/tested_people), span = 0.05) + 
                             xlim(-5,100) + ylim(0,.25) + theme_bw() #there are some outliars north of 10


options(gghighlight_max_labels = 60)
detach(gghighlight)
library(gghighlight)
library(data.table)
geonames <- fread("/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/allCountries.txt", sep="\t") %>% mutate_if(is.numeric,as.character) %>% mutate_if(is.factor,as.character)
dim(geonames) #12,006,426

geonames_small <- geonames %>% dplyr::select(geonameid=V1, name_text=V2) %>% inner_join( lhs_long_clean_imputed %>% dplyr::select(geonameid) %>% distinct() )
dim(geonames_small)


df_slopes %>% left_join(geonames_small  ) %>% mutate(name_text %>% as.factor()) %>% filter(gid %>% str_detect("USA\\.[0-9]{1,2}_1$")) %>%
           ggplot() + geom_line(aes(x=t_alligned, y=confirmed_log_y_hat, color=name_text), alpha=.2) + 
                             gghighlight(max_highlight = 100L,
                                         gid %>% str_detect("USA"), unhighlighted_params=list(alpha=.1),
                                         use_direct_label=T) +
                             geom_smooth( aes(x=t_alligned , y=confirmed_log_y_hat, span = 0.05)) + 
                             xlim(-5,100) + #ylim(0,.25) + 
                             theme_bw() #there are some outliars north of 10

df_slopes %>% left_join(geonames_small  ) %>% mutate(name_text %>% as.factor()) %>% filter(gid %>% str_detect("USA\\.[0-9]{1,2}_1$")) %>%
           ggplot() + geom_line(aes(x=t_alligned, y=tested_people_log_y_hat, color=name_text), alpha=.2) + 
                             gghighlight(max_highlight = 100L,
                                         gid %>% str_detect("USA"), unhighlighted_params=list(alpha=.1),
                                         use_direct_label=T) +
                             geom_smooth( aes(x=t_alligned , y=tested_people_log_y_hat, span = 0.05)) + 
                             xlim(-5,150) + #ylim(0,.25) + 
                             theme_bw() #there are some outliars north of 10

df_slopes %>% left_join(geonames_small  ) %>% mutate(name_text %>% as.factor()) %>%
           ggplot() + geom_line(aes(x=t_alligned, y=exp(confirmed_log_y_hat)/exp(tested_people_log_y_hat), color=name_text), alpha=.2) + 
                             gghighlight(max_highlight = 100L,
                                         gid %>% str_detect("USA"), unhighlighted_params=list(alpha=.1),
                                         use_direct_label=T) +
                             geom_smooth( aes(x=t_alligned , y=exp(confirmed_log_y_hat)/exp(tested_people_log_y_hat), span = 0.05)) + 
                             xlim(-5,100) + ylim(0,.25) + theme_bw() #there are some outliars north of 10

#we might need to switch this to fd. Because it's the percent of the new tests coming back positive not the old ones.

#Remind ourselves interpretations when we lhs
#https://data.library.virginia.edu/interpreting-log-transformations-in-a-linear-model/

```





```{r, eval=F}

lm1 <- lm(deaths ~  confirmed,data=lhs_long_median %>% filter(CFR<1))
lm2 <- lm(deaths ~ tested_people + confirmed,data=lhs_long_median %>% filter(CFR<1))

library(ggRandomForests); #install.packages('ggRandomForests')
rf1 <- rfsrc(deaths~ confirmed,
                      data=lhs_long_median %>% filter(CFR<1) %>% as.data.frame() )
gg_e <- gg_error(rf1)
gg_v <- gg_variable(rf1)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4) 

rf1 <- rfsrc(deaths~ confirmed + tested_people,
                      data=lhs_long_median %>% filter(CFR<1) %>% as.data.frame() )
gg_e <- gg_error(rf1)
gg_v <- gg_variable(rf1)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)


rf2 <- rfsrc(CFR ~  tested_people_log,
                      data=lhs_long_median %>% 
                      mutate(CFR=deaths/confirmed) %>%
                      mutate(tested_people_log=log(tested_people+1)) %>%
                      filter(CFR<1) %>% as.data.frame()
             )
gg_v <- gg_variable(rf2)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)

rf2 <- rfsrc(CFR ~  tested_people_log + confirmed_log,
                      data=lhs_long_median %>% 
                      mutate(CFR=deaths/confirmed) %>%
                      mutate(tested_people_log=log(tested_people+1)) %>%
                      mutate(confirmed_log=log(confirmed+1)) %>%
                      filter(CFR<1) %>% as.data.frame()
             )
gg_v <- gg_variable(rf2)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)


#Throw in time
rf3 <- rfsrc(CFR ~  positive_perc + tested_people_log,
                                    data=lhs_long_median %>% 
                                    mutate(CFR=deaths/confirmed) %>%
                                    mutate(positive_perc=confirmed/tested_people) %>%
                                    mutate(tested_people_log=log(tested_people+1)) %>%
                                    mutate(confirmed_log=log(confirmed+1)) %>%
                                    filter(CFR<1) %>% as.data.frame()
             )
gg_v <- gg_variable(rf3)
plot(gg_v, panel=TRUE, se=.95, span=1.2, alpha=.4)

partial_Boston <- plot.variable(rf3,
partial=TRUE, sorted=FALSE,
show.plots = FALSE )

gg_p <- gg_partial(partial_Boston)
plot(gg_p, panel=TRUE, points = F )


copper_cts <-quantile_pts(lhs_long_median$tested_people_log, groups = 6, intervals = TRUE)
partial_coplot_Boston <- gg_partial_coplot(rf2, xvar="confirmed_log",
                                         groups=rm_grp,
                                         show.plots=FALSE)



summary(lhs_long_median$CFR)

lhs_long_median %>% filter(confirmed>10) %>% filter(CFR<1) %>% pull(CFR) %>% summary() #0.0297297  that's a median CFR of about 3%
lhs_long_median %>% filter(confirmed>10) %>% filter(CFR<1) %>% pull(CFR) %>% hist(breaks=50)

lhs_long_median %>% filter(confirmed>10) %>% filter(CFR<1) %>% ggplot(aes(x=CFR)) + geom_density()


```



```{r, eval=F}
#This code is now depricated in favor of the tree base method above

places <- lhs_long_clean$wikidata_id %>% unique() %>% na.omit() ; length(places)
datasets <- lhs_long_clean$dataset %>% unique() %>% na.omit() ; table(dataset)

temp_list <- list()
for(p in places){
    print(p)
    for(d in datasets){
      temp <- NULL
      temp <- lhs_long_clean %>% 
              arrange(date_asdate) %>% 
              filter(dataset %in% d) %>% 
              filter(wikidata_id %in% p) %>% 
              mutate(confirmed_log=log(confirmed+1)) %>%
              mutate(deaths_log=log(deaths+1)) %>%
              mutate(tested_people_log=log(tested_people+1)) %>%
              mutate(t= as.numeric(date_asdate) - min(as.numeric(date_asdate)) +1  ) %>% #start at 1 to make indexing easier
              mutate(i= row_number()  ) # actually need this
        
      i_original <- temp$i
      
      if( nrow(temp)==0 ) { next} #print("error");
      print(p)
      
      #bp <- breakpoints(confirmed_log ~ 1, data=temp)
      bp <- NULL
      lm1 <- NULL
      y_hat <- NA
      cdf <- NULL
      try({
            #if it fails fall back to just a lm
        lm1 <- lm(confirmed_log ~ 1 + t, data=temp)
    
        cdf <- data.frame(
                          t= 1, 
                          confirmed_log_intercept= coef(lm1)[1],
                          confirmed_log_slope= coef(lm1)[2],
                          confirmed_log_slope_break=0
                          )
        confirmed_log_y_hat=fitted.values(lm1)
      })
      
      try({
        bp <- breakpoints(confirmed_log ~ 1 + t, data=temp, h=3)
        
        cdf <- data.frame(
                          i= c(1,bp$breakpoints), 
                          confirmed_log_intercept= coef(bp)[,1],
                          confirmed_log_slope= coef(bp)[,2],
                          confirmed_log_slope_break=1
                          )
        confirmed_log_y_hat=fitted.values(bp)
      })
      
      try({
        temp2 <- temp
        temp2$confirmed_log_y_hat <- NA
        temp2$confirmed_log_y_hat <- confirmed_log_y_hat
        
        temp2 <- temp2 %>% 
                left_join(cdf) 
        q=paste0(p,"_",d)
        temp2 <- temp2 %>% 
                  expand(dataset, gid,geonameid, wikidata_id,t=min(temp$t):max(temp$t)) %>% #expand this to include all the t 
                  left_join(temp2) %>% 
                  mutate(date_asdate=min(date_asdate, na.rm=T)-1+t) %>% #go back interp date again
                  mutate(confirmed_log_slope_break = confirmed_log_slope_break %>% replace_na(0) %>% cumsum() ) %>%
                  fill(confirmed_log_intercept) %>%
                  fill(confirmed_log_slope) %>%
                  mutate(confirmed_log_y_hat= confirmed_log_intercept + confirmed_log_slope*t ) %>% 
                  mutate(confirmed_log_slope_percent_change = round((exp(confirmed_log_slope)-1)*100,2)) 

        temp_list[[as.character(q)]] <- temp2
      })
      #if( is.na( temp_list[[as.character(q)]]$y_hat) ) {print("error"); break}
    }
}
#"13055"
temp_df <- bind_rows(temp_list)
dim(temp_df) #bigger because we're interpolating now

# install.packages("devtools")
#devtools::install_github("tidyverse/multidplyr")

library(multidplyr)
library(dplyr, warn.conflicts = FALSE)
#cluster <- new_cluster(4)

#need to supress messages
rex_function <- function(x){
  temp=data.frame(confirmed_log_slope=x)
  tryCatch({
    #if there's an NA in x we get an error because breakpoints is one short
    prediction <- breakpoints(formula=confirmed_log_slope ~ 1, data=temp, h=3) %>% fitted.values()
    result=rep(NA, length(x) )
    result[!is.na(x)] <-prediction
    return(result)
    
  }, error=function(e){})
  
  return( rep(NA, length(x) ) )
         
}
#didn't take too long now

#library(multidplyr)
#library(dplyr, warn.conflicts = FALSE)
#cluster <- new_cluster(4)

library(tictoc)
tic()
library(strucchange)
test <- temp_df %>% head(20000)
test3 <- test %>% 
                group_by(wikidata_id) %>% 
                #partition(cluster) %>%
                arrange(date_asdate) %>%
                #mutate( confirmed_log_slope_y_hat = breakpoints(formula=confirmed_log_slope ~ 1, data=. ) %>% fitted.values() ) %>% 
                mutate( confirmed_log_slope_y_hat = rex_function(confirmed_log_slope) ) %>%  #fails on the cluster
                ungroup() #%>%
                #collect()
toc() #48 seconds for 10k #122 seconds for 20k

saveRDS(temp_df, "/media/skynet2/905884f0-7546-4273-9061-12a790830beb/rwd_github_private/NESScovid19/data_temp/lhs_long_interpolated.Rds")

```









```{r, eval=F, echo=F,warning=FALSE,message=FALSE,error=FALSE}

#Here is where I worked out the idea of fitting piecewise linear functions to each dataset independently. It's now replaced with a fully automated pipline based on trees.

count_datasets <- lhs_long_clean %>% dplyr::select(wikidata_id, dataset) %>% distinct() %>% count(wikidata_id)

formula = confirmed_log ~ 1 + date_rank #+ I(date_rank^2) + I(date_rank^3)
h=5
#bexar county
library(strucchange) ; #install.packages('strucchange')
temp <- lhs_long_clean %>% arrange(date_asdate) %>% filter(wikidata_id %in% "Q16861") %>%  mutate(confirmed_log=log(confirmed+1)) %>%
        mutate(date_rank= as.numeric(date_asdate) - min(as.numeric(date_asdate)))  

table(temp$dataset)

temp_usafacts <- temp %>% filter(dataset=="usafacts")
bp_usafacts <- breakpoints(formula, data=temp_usafacts,h=h)
temp_usafacts$y_hat <- fitted.values(bp_usafacts)
temp_usafacts$groups <- 0; temp_usafacts$groups[bp_usafacts$breakpoints+1] <- 1 ; temp_usafacts$groups <- cumsum(temp_usafacts$groups)+1

temp_nyt <- temp %>% filter(dataset=="nyt")
bp_nyt <- breakpoints(formula, data=temp_nyt,h=h)
temp_nyt$y_hat <- fitted.values(bp_nyt)
temp_nyt$groups <- 0; temp_nyt$groups[bp_nyt$breakpoints+1] <- 1 ; temp_nyt$groups <- cumsum(temp_nyt$groups)+1


temp_CSSE <- temp %>% filter(dataset=="CSSE")
bp_CSSE <- breakpoints(formula, data=temp_CSSE,h=h)
temp_CSSE$y_hat <- fitted.values(bp_CSSE)
temp_CSSE$groups <- 0; temp_CSSE$groups[bp_CSSE$breakpoints+1] <- 1 ; temp_CSSE$groups <- cumsum(temp_CSSE$groups)+1

temp_bing <- temp %>% filter(dataset=="bing")
bp_bing <- breakpoints(formula, data=temp_bing,h=h) #this fails bc too few
lm_bing <-lm(formula, data=temp_bing)
temp_bing$y_hat <- fitted.values(lm_bing)
temp_bing$groups <-1

temp_df <- bind_rows(temp_nyt, temp_CSSE, temp_bing, temp_usafacts)  %>% 
            arrange(dataset,date_asdate) %>% 
           mutate(groups=paste(dataset,groups))

temp_df2 <- bind_rows(temp_nyt, temp_CSSE, temp_bing, temp_usafacts) %>% 
            arrange(date_asdate) %>% 
            group_by(date_asdate) %>%
              summarise(y_hat_mean=mean(y_hat, na.rm=T)) %>%
            mutate(groups=99)

ggplot() + 
   geom_point(data=temp_df, aes(x=date_asdate, y=confirmed_log, color=dataset)) +
   geom_line(data=temp_df, aes(x=date_asdate, y=y_hat, color=dataset, group=groups)) +
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("Bexar County (Q16861) Confirmed")



#####
#China Q148
temp <- lhs_long_clean %>% arrange(date_asdate) %>% 
        filter(wikidata_id %in% "Q148") %>% 
        group_by(dataset, date_asdate) %>%
          summarise(confirmed=max(confirmed, na.rm=T) ) %>% #this is a problem dupes with the same date  #this fixes a lot of things but we need to figure out the origin of this problem
        ungroup() %>%
        mutate(confirmed_log=log(confirmed+1)) %>% 
        mutate(date_rank= as.numeric(date_asdate) - min(as.numeric(date_asdate)))  


temp_wikipedia <- temp %>% filter(dataset=="wikipedia")
bp_wikipedia <- breakpoints(formula, data=temp_wikipedia,h=h)
temp_wikipedia$y_hat <- fitted.values(bp_wikipedia)
temp_wikipedia$groups <- 0; temp_wikipedia$groups[bp_wikipedia$breakpoints+1] <- 1 ; temp_wikipedia$groups <- cumsum(temp_wikipedia$groups)+1


temp_ecdc <- temp %>% filter(dataset=="ecdc")
bp_ecdc <- breakpoints(formula, data=temp_ecdc,h=h)
temp_ecdc$y_hat <- fitted.values(bp_ecdc)
temp_ecdc$groups <- 0; temp_ecdc$groups[bp_ecdc$breakpoints+1] <- 1 ; temp_ecdc$groups <- cumsum(temp_ecdc$groups)+1

temp_who <- temp %>% filter(dataset=="who")
bp_who <- breakpoints(formula, data=temp_who,h=h)
temp_who$y_hat <- fitted.values(bp_who)
temp_who$groups <- 0; temp_who$groups[bp_who$breakpoints+1] <- 1 ; temp_who$groups <- cumsum(temp_who$groups)+1


temp_metabiota <- temp %>% filter(dataset=="metabiota")
bp_metabiota <- breakpoints(formula, data=temp_metabiota,h=h)
temp_metabiota$y_hat <- fitted.values(bp_metabiota)
temp_metabiota$groups <- 0; temp_metabiota$groups[bp_metabiota$breakpoints+1] <- 1 ; temp_metabiota$groups <- cumsum(temp_metabiota$groups)+1

temp_bing <- temp %>% filter(dataset=="bing")
bp_bing <- breakpoints(formula, data=temp_bing,h=h) #this fails bc too few
lm_bing <-lm(formula, data=temp_bing)
temp_bing$y_hat <- fitted.values(lm_bing)
temp_bing$groups <-1

temp_df <- bind_rows(temp_ecdc, temp_metabiota, temp_bing, temp_who, temp_wikipedia) %>% 
            arrange(dataset,date_asdate) %>% 
           mutate(groups=paste(dataset,groups))

temp_df2 <- bind_rows(temp_ecdc, temp_metabiota, temp_bing, temp_who, temp_wikipedia) %>% 
            arrange(date_asdate) %>% 
            group_by(date_asdate) %>%
              summarise(y_hat_mean=mean(y_hat, na.rm=T)) %>%
            mutate(groups=99)

ggplot() + 
   geom_point(data=temp_df, aes(x=date_asdate, y=confirmed_log, color=dataset)) +
   geom_line(data=temp_df, aes(x=date_asdate, y=y_hat, color=dataset, group=groups)) +
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("China (Q148) Confirmed")
   #geom_vline(xintercept=bp$breakpoints)


#India
#"Q668"
temp <- lhs_long_clean %>% arrange(date_asdate) %>% 
        filter(wikidata_id %in% "Q668") %>% 
        group_by(dataset, date_asdate) %>%
          summarise(confirmed=max(confirmed, na.rm=T) ) %>% #this is a problem dupes with the same date  #this fixes a lot of things but we need to figure out the origin of this problem
        ungroup() %>%
        mutate(confirmed_log=log(confirmed+1)) %>% 
        mutate(date_rank= as.numeric(date_asdate) - min(as.numeric(date_asdate)))  
table(temp$dataset)

temp_covid19india <- temp %>% filter(dataset=="covid19india")
bp_covid19india <- breakpoints(formula, data=temp_covid19india,h=h)
temp_covid19india$y_hat <- fitted.values(bp_covid19india)
temp_covid19india$groups <- 0; temp_covid19india$groups[bp_covid19india$breakpoints+1] <- 1 ; temp_covid19india$groups <- cumsum(temp_covid19india$groups)+1

temp_wikipedia <- temp %>% filter(dataset=="wikipedia")
bp_wikipedia <- breakpoints(formula, data=temp_wikipedia,h=h)
temp_wikipedia$y_hat <- fitted.values(bp_wikipedia)
temp_wikipedia$groups <- 0; temp_wikipedia$groups[bp_wikipedia$breakpoints+1] <- 1 ; temp_wikipedia$groups <- cumsum(temp_wikipedia$groups)+1

temp_CSSE <- temp %>% filter(dataset=="CSSE")
bp_CSSE <- breakpoints(formula, data=temp_CSSE,h=h)
temp_CSSE$y_hat <- fitted.values(bp_CSSE)
temp_CSSE$groups <- 0; temp_CSSE$groups[bp_CSSE$breakpoints+1] <- 1 ; temp_CSSE$groups <- cumsum(temp_CSSE$groups)+1

temp_ecdc <- temp %>% filter(dataset=="ecdc")
bp_ecdc <- breakpoints(formula, data=temp_ecdc,h=h)
temp_ecdc$y_hat <- fitted.values(bp_ecdc)
temp_ecdc$groups <- 0; temp_ecdc$groups[bp_ecdc$breakpoints+1] <- 1 ; temp_ecdc$groups <- cumsum(temp_ecdc$groups)+1

temp_who <- temp %>% filter(dataset=="who")
bp_who <- breakpoints(formula, data=temp_who,h=h)
temp_who$y_hat <- fitted.values(bp_who)
temp_who$groups <- 0; temp_who$groups[bp_who$breakpoints+1] <- 1 ; temp_who$groups <- cumsum(temp_who$groups)+1

temp_metabiota <- temp %>% filter(dataset=="metabiota")
bp_metabiota <- breakpoints(formula, data=temp_metabiota,h=h)
temp_metabiota$y_hat <- fitted.values(bp_metabiota)
temp_metabiota$groups <- 0; temp_metabiota$groups[bp_metabiota$breakpoints+1] <- 1 ; temp_metabiota$groups <- cumsum(temp_metabiota$groups)+1

temp_bing <- temp %>% filter(dataset=="bing")
bp_bing <- breakpoints(formula, data=temp_bing,h=h) #this fails bc too few
lm_bing <-lm(formula, data=temp_bing)
temp_bing$y_hat <- fitted.values(lm_bing)
temp_bing$groups <-1

temp_df <- bind_rows(temp_covid19india, temp_wikipedia, temp_CSSE, temp_ecdc, temp_who, temp_metabiota, temp_bing) %>% 
            arrange(dataset,date_asdate) %>% 
           mutate(groups=paste(dataset,groups))

temp_df2 <- bind_rows(temp_covid19india, temp_wikipedia, temp_CSSE, temp_ecdc, temp_who, temp_metabiota, temp_bing) %>% 
            arrange(date_asdate) %>% 
            group_by(date_asdate) %>%
              summarise(y_hat_mean=mean(y_hat, na.rm=T)) %>%
            mutate(groups=99)

ggplot() + 
   geom_point(data=temp_df, aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
   geom_line(data=temp_df, aes(x=date_asdate, y=y_hat, color=dataset, group=groups)) +
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("India (Q668) Confirmed")


#Florida
#Q812
temp <- lhs_long_clean %>% arrange(date_asdate) %>% 
        filter(wikidata_id %in% "Q812") %>% 
        group_by(dataset, date_asdate) %>%
          summarise(confirmed=max(confirmed, na.rm=T) ) %>% #this is a problem dupes with the same date  #this fixes a lot of things but we need to figure out the origin of this problem
        ungroup() %>%
        mutate(confirmed_log=log(confirmed+1)) %>%
        mutate(date_rank= as.numeric(date_asdate) - min(as.numeric(date_asdate)))  
table(temp$dataset)

temp_covidtracking <- temp %>% filter(dataset=="covidtracking")
bp_covidtracking <- breakpoints(formula, data=temp_covidtracking,h=h)
temp_covidtracking$y_hat <- fitted.values(bp_covidtracking)
temp_covidtracking$groups <- 0; temp_covidtracking$groups[bp_covidtracking$breakpoints+1] <- 1 ; temp_covidtracking$groups <- cumsum(temp_covidtracking$groups)+1

temp_wikipedia <- temp %>% filter(dataset=="wikipedia")
bp_wikipedia <- breakpoints(formula, data=temp_wikipedia,h=h)
temp_wikipedia$y_hat <- fitted.values(bp_wikipedia)
temp_wikipedia$groups <- 0; temp_wikipedia$groups[bp_wikipedia$breakpoints+1] <- 1 ; temp_wikipedia$groups <- cumsum(temp_wikipedia$groups)+1

temp_metabiota <- temp %>% filter(dataset=="metabiota")
bp_metabiota <- breakpoints(formula, data=temp_metabiota,h=h)
temp_metabiota$y_hat <- fitted.values(bp_metabiota)
temp_metabiota$groups <- 0; temp_metabiota$groups[bp_metabiota$breakpoints+1] <- 1 ; temp_metabiota$groups <- cumsum(temp_metabiota$groups)+1

temp_bing <- temp %>% filter(dataset=="bing")
bp_bing <- breakpoints(formula, data=temp_bing,h=h) #this fails bc too few
lm_bing <-lm(formula, data=temp_bing)
temp_bing$y_hat <- fitted.values(lm_bing)
temp_bing$groups <-1

temp_df <- bind_rows(temp_covidtracking, temp_wikipedia, temp_metabiota, temp_bing) %>% 
            arrange(dataset,date_asdate) %>% 
           mutate(groups=paste(dataset,groups))

temp_df2 <- bind_rows(temp_covidtracking, temp_wikipedia, temp_metabiota, temp_bing) %>% 
            arrange(date_asdate) %>% 
            group_by(date_asdate) %>%
              summarise(y_hat_mean=mean(y_hat, na.rm=T)) %>%
            mutate(groups=99)

ggplot() + 
   geom_point(data=temp_df, aes(x=date_asdate, y=confirmed_log, color=dataset), alpha=.3) +
   geom_line(data=temp_df, aes(x=date_asdate, y=y_hat, color=dataset, group=groups)) +
   #geom_line(data=temp_df2, aes(x=date_asdate, y=y_hat_mean), color="black", linetype = "dashed") +
   theme_bw() +
   ggtitle("Florida (Q812) Confirmed")

```



```{r, eval=F, echo=F,warning=FALSE,message=FALSE,error=FALSE}
#As was this part
# # Q668  Q16861  Q148  Q30 Q148 Q16861 Q30
temp <- temp_df %>% filter(wikidata_id=="Q148") %>% mutate(date_asnumeric=as.numeric(date_asdate))
temp %>% ggplot() + geom_point(aes(date_asdate,confirmed_log))  + facet_wrap(~dataset) #+ geom_point(aes(date_asdate,confirmed_log_y_hat), color="blue")

#library(rpart)
#rt <- rpart(confirmed_log ~ 1 + date_asdate , data=temp)
#temp$y_hat <- predict( rt, newdata=temp )

#temp$dataset <- as.factor(temp$dataset)
#fmBH <- mob(confirmed_log ~ 1 + t + I(t^2) + I(t^3) | t + dataset, control = mob_control(minsplit = 3), data = temp,  model = linearModel) #this fits a piecewise linear to each dataset #have to use t, doesn't like data objects
#plot(fmBH)
#coef(fmBH)
#temp$y_hat <- predict( fmBH, newdata=temp )

#The trick is fitting only to one dataset at a time
temp_list <- list()
for( d in temp$dataset %>% unique() ){
  temptemp <- temp %>% filter(dataset==d)
  fmBH <- mob(confirmed_log ~ 1 + date_asnumeric    | date_asnumeric , control = mob_control(minsplit = 3, alpha=.2), data = temptemp,  model = linearModel)  # + I(date_asnumeric^2)     + I(t^3)  # + I(t^2) + I(t^3) + I(t^4) + I(t^2) 
  temptemp$y_hat <- predict( fmBH, newdata=temptemp , type = c("response") )
  temptemp$group_number <-  predict( fmBH, newdata=temptemp , type = c("node"))
  temptemp <- temptemp %>% group_by(group_number) %>% arrange(date_asnumeric) %>% mutate(slope=tsibble::difference(y_hat)) %>% fill(slope, .direction="up") %>% ungroup() %>%
                      mutate(percent_change = round((exp(slope)-1)*100,2)) 

  temp_list[[d]] <- temptemp
}

combined <- bind_rows(temp_list) 
p0 <- combined %>%
     mutate(group=paste(wikidata_id, dataset, group_number)) %>%
     ggplot() + geom_point(aes(date_asdate,confirmed_log, color=dataset), alpha=.5) + 
                                    geom_line(aes(date_asdate,y_hat, group=group), color="blue", alpha=1) + facet_wrap(~dataset) + theme_bw() #it shits the bed in that big gap in wikipedia

fmBH_slope <- mob(confirmed_log ~ 1   | date_asnumeric , control = mob_control(minsplit = 3, alpha=.1), data = combined,  model = linearModel)  #it doesn't want to do 
rt1 <- rpart(formula = percent_change ~ date_asnumeric, data=combined)
combined$percent_change_y_hat <-  predict(rt1, newdata=combined)
#combined$slope_y_hat <- predict( fmBH_slope, newdata=combined  , type = c("response") )
#combined$group_number <- predict( fmBH_slope, newdata=combined  , type = c("node") )

p1 <- combined %>%
     mutate(group=paste(wikidata_id, dataset, group_number)) %>%
     ggplot() + geom_point(aes(date_asdate,confirmed_log, color=dataset), alpha=.5) + 
                                    geom_line(aes(date_asdate,y_hat, group=group, color=dataset), alpha=.1) + theme_bw() + facet_wrap(~dataset)#  #it shits the bed in that big gap in wikipedia


p2 <- combined %>% ggplot() + geom_point(aes(date_asdate,percent_change_y_hat, color=dataset), alpha=.5) + 
             geom_line(aes(date_asdate,percent_change_y_hat), color="black", alpha=1) + theme_bw() + facet_wrap(~dataset) #+ facet_wrap(~dataset) #it shits the bed in that big gap in wikipedia

p1 / p2


```


